[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_ff447803-2681-47b3-852d-b390607717da":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:ff447803-2681-47b3-852d-b390607717da,\"}":105},{"code":4,"data":5,"meta":20},"SUCCESS",{"id":6,"createTime":7,"updateTime":8,"relativeEntities":9,"slug":10,"properties":11,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":22,"manageAffiliations":41,"indexDatabases":57,"url":94,"thumbnailPath":20,"statistic":95,"gsStatistic":20,"type":104,"analyzePriority":20},"ff447803-2681-47b3-852d-b390607717da","2024-04-11T03:42:55.907+00:00","2025-11-21T09:59:37.918+00:00",[],"Computational-Statistics",{"issn":12,"title":14,"eissn":16},{"VOID":13},"1613-9658",{"EN":15},"Computational Statistics",{"VOID":17},"0943-4062","PUBLISHER","PENDING",null,0,[23,29,35],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":25,"label":26,"description":28,"parentId":20,"standard":20,"scholarHubFieldId":20},"0bfb7244-5877-4f77-9f03-0a51be00abe5",[],{"EN":27},"Computational Mathematics",{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":31,"label":32,"description":34,"parentId":20,"standard":20,"scholarHubFieldId":20},"f8634701-aae0-4ecc-87c8-b1119c426fed",[],{"EN":33},"Statistics, Probability and Uncertainty",{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":37,"label":38,"description":40,"parentId":20,"standard":20,"scholarHubFieldId":20},"c6c03dad-ccb9-49c4-a5fa-4ad15f69d436",[],{"EN":39},"Statistics and Probability",{},[42,50],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":44,"slug":20,"properties":45,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":48,"statistic":20},"6b6d67d8-1887-4ca8-86c5-a38f66f10928",[],{"title":46},{"EN":47},"Springer Verlag",[49],"9a7c7208-b28a-42c2-a634-5a7f90eee3ab",{"id":51,"createTime":20,"updateTime":20,"relativeEntities":52,"slug":20,"properties":53,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":56,"statistic":20},"869fc292-62ea-48f4-960e-51fea58b02ba",[],{"title":54},{"EN":55},"Springer Heidelberg",[49],[58,75],{"id":59,"indexDatabase":60,"url":72,"indexYears":20,"academicFieldIds":73,"indexDatabaseRanking":20},"5cca47c0-150e-4179-a1aa-dff26983dca1",{"id":61,"createTime":20,"updateTime":20,"relativeEntities":62,"label":63,"description":65,"key":68,"publicationTags":69,"standard":20},"a4921856-b128-4d9f-8f1f-e80813d3bbd4",[],{"EN":64,"VI":64},"ISI\u002FSCIE - Science Citation Index Expanded",{"EN":66,"VI":67},"SCIE database","Cơ sở dữ liệu SCIE","scie",[70,71],"SCIE","ISI","https:\u002F\u002Fmjl.clarivate.com\u002Fsearch-results?issn=0943-4062",[74],"a8ba2ff8-7a94-43da-936b-fea740ae9de1",{"id":76,"indexDatabase":77,"url":87,"indexYears":88,"academicFieldIds":89,"indexDatabaseRanking":93},"8cafe07b-3d31-41e3-aebb-3a8627efa5c5",{"id":78,"createTime":20,"updateTime":20,"relativeEntities":79,"label":80,"description":82,"key":84,"publicationTags":85,"standard":20},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9",[],{"EN":81,"VI":81},"Scopus - Elsevier",{"EN":81,"VI":83},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[86],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F28930","1996-2025",[90,91,92],"0ae9247a-0be3-46d8-a17e-488fa168abcc","b63de939-dadd-4939-b5dc-10282f8b39be","39bf4935-81d1-4b13-842c-e6e38874ca5e","SCOPUS__Q3","https:\u002F\u002Flink.springer.com\u002Fjournal\u002F180",{"impactFactor":21,"impactFactorByYear":96,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":98,"totalCitation":21,"totalCitationByYear":102,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":103,"hindexLast5Year":21,"hindex":21},{},21,{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},1,2,3,{},{},"JOURNAL",{"meta":106,"data":108},{"total":107},"1140",[109,237,355,504,635,768,878,1080,1177,1299],{"id":110,"createTime":111,"updateTime":112,"relativeEntities":113,"slug":114,"properties":115,"entityType":125,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":126,"viewCount":21,"primaryUrl":128,"fullTextUrl":20,"authors":129,"publicationType":176,"publisherRelationship":177,"citationCount":20,"citationInfo":20,"publishDate":233,"publishYear":234,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":235,"openAccess":20,"references":20,"isForceReanalyzing":236},"4001bcfe-bce0-48b3-baec-af2f91bb9216","2024-02-07T22:35:24.482+00:00","2026-09-08T10:14:48.109+00:00",[],"Bayesian-multilevel-logistic-regression-models-a-case-study-applied-to-the-results-of-two-questionnaires-administered-to-university-students",{"abstract":116,"title":118,"references":121,"doi":123},{"EN":117},"Bayesian multilevel models—also known as hierarchical or mixed models—are used in situations in which the aim is to model the random effect of groups or levels. In this paper, we conduct a simulation study to compare the predictive ability of 1-level Bayesian multilevel logistic regression models with that of 2-level Bayesian multilevel logistic regression models by using the prior Scaled Beta2 and inverse-gamma distributions to model the standard deviation in the 2-level. Then, these models are employed to estimate the correct answers in two questionnaires administered to university students throughout the first academic semester of 2018. The results show that 2-level models have a better predictive ability and provide more precise probability intervals than 1-level models, particularly when the prior Scaled Beta2 distribution is used to model the standard deviation in the second level. Moreover, the probability intervals of 1-level Bayesian multilevel logistic regression models proved to be more precise when Scaled Beta2 distributions, rather than an inverse-gamma distribution, are employed to model the standard deviation or when 1-level Bayesian multilevel logistic regression models, are used.",{"EN":119,"VI":120},"Bayesian multilevel logistic regression models: a case study applied to the results of two questionnaires administered to university students","Các mô hình hồi quy logistic đa mức Bayes: nghiên cứu trường hợp áp dụng cho kết quả của hai bảng câu hỏi khảo sát sinh viên đại học",{"VOID":122},"Ayalew MM (2020) Bayesian hierarchical analyses for entrepreneurial intention of students. J Big Data 7:711–23\nAychiluhm SB, Gelaye KA, Angaw DA, Dagne GA, Tadesse AW, Abera A, Dillu D (2020) Determinants of malaria among under-five children in Ethiopia: Bayesian multilevel analysis. BMC Public Health 20:10–2011\nBerger J (2006) The case for objective Bayesian analysis. Bayesian Anal 1(3):385–402\nBernardo J, Smith A (2000) Bayesian theory. Wiley, New York\nBirlutiu A, Groot P, Heskes T (2010) Multi-task preference learning with an application to hearing aid personalization. Neurocomputing 73(7–9):1177–1185\nBornmann L, Stefaner M, de Moya Anegón F, Mutz R (2016) Excellence networks in science: A web-based application based on Bayesian multilevel logistic regression (bmlr) for the identification of institutions collaborating successfully. J Informet 10(1):312–327\nBrooks S, Roberts G (1998) Assessing convergence of Markov chain Monte Carlo algorithms. Stat Comput 8(4):319–335\nCowles M, Carlin B (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review. J Am Stat Assoc 91(434):883–904\nDe la Cruz R, Meza C, Arribas-Gil A, Carroll R (2016) Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements. J Multivar Anal 143:94–106\nFagbamigbe AF, Uthman AO, Ibisomi L (2021) Hierarchical disentanglement of contextual from compositional risk factors of diarrhoea among under-five children in low-and middle-income countries. Sci Rep 11(1):1–17\nGañan-Cardenas E, Jiménez JC, Pemberthy-R JI (2021) Bayesian hierarchical modeling of operating room times for surgeries with few or no historic data. J Clin Monit Comput 36:1–16\nGaviria J, Morera M (2005) Modelos jerárquicos lineales. Editorial La Muralla\nGelman A (2006) Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper). Bayesian Anal 1:13515–534\nGelman A, Hill J (2006) Data analysis using regression and multilevel\u002Fhierarchical models. Cambridge University Press, Cambridge\nGelman A, Carlin J, Stern H, Dunson D, Vehtari A, Rubin D (2013) Bayesian data analysis, 3rd edn. Chapman and Hall\u002FCRC, Reading\nGrogan-Kaylor A, Castillo B, Pace GT, Ward KP, Ma J, Lee SJ, Knauer H (2021) Global perspectives on physical and nonphysical discipline: a Bayesian multilevel analysis. Int J Behav Dev 45(3):216–225\nJabessa S, Jabessa D (2021) Bayesian multilevel model on maternal mortality in Ethiopia. J Big Data 8(1):1–17\nJara A, Quintana F, San Martín E (2008) Linear mixed models with skew-elliptical distributions: a Bayesian approach. Comput Stat Data Anal 52(11):5033–5045\nKing G, Zeng L (2001) Logistic regression in rare events data. Polit Anal 9(2):137–163\nKwiatkowski D, Phillips P, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? J Econom 54(1–3):159–178\nLlera A, Beckmann C (2016) Estimating an inverse gamma distribution. arXiv:1605.01019\nLu Z-H, Khondker Z, Ibrahim JG, Wang Y, Zhu H, Initiative ADN (2017) Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies. Neuroimage 149:305–322\nMcElreath R (2015) Statistical rethinking: a Bayesian course with examples in r and stan. Chapman and Hall\u002FCRC, New York\nMłynarczyk D, Armero C, Gómez-Rubio V, Puig P (2021) Bayesian analysis of population health data. Mathematics 9(5):577\nNtzoufras I (2011) Bayesian modeling using winbugs, vol 698. Wiley, New York\nPeng C-Y, Lee K, Ingersoll G (2002) An introduction to logistic regression analysis and reporting. J Educ Res 96(1):3–14\nPérez M-E, Pericchi L, Ramírez I (2017) The scaled beta2 distribution as a robust prior for scales. Bayesian Anal 12(3):615–637\nPinheiro J, Bates D (2006) Mixed-effects models in S and S-PLUS mixed-effects models in s and s-plus. Springer, Berlin\nPregibon D (1981) Logistic regression diagnostics logistic regression diagnostics. Ann Stat 9(4):705–724\nR Core Team (2019) R: a language and environment for statistical computing [Computer software manual]. Vienna, Austria. https:\u002F\u002Fwww.R-project.org\u002F\nRojas J, Ramírez I (2019) Ajuste de un modelo jerárquico desde un enfoque bayesiano (Unpublished master’s thesis). Universidad Nacional de Colombia-Sede Medellín\nSherwood RJ, Oh HS, Valiathan M, McNulty KP, Duren DL, Knigge RP, Middleton KM (2021) Bayesian approach to longitudinal craniofacial growth: the craniofacial growth consortium study. Anat Rec 304(5):991–1019\nSpiegelhalter D, Best N, Carlin B, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B (Stat Methodol) 64(4):583–639\nSturtz S, Ligges U, Gelman A (2010) R2openbugs: a package for running openbugs from r. http:\u002F\u002Fcran.rproject.org\u002Fweb\u002Fpackages\u002FR2OpenBUGS\u002Fvignettes\u002FR2OpenBUGS. pdf)\nTang N-S, Duan X-D (2014) Bayesian influence analysis of generalized partial linear mixed models for longitudinal data. J Multivar Anal 126:86–99\nTrapletti A, Hornik K, LeBaron B, Hornik M (2019) Package ‘tseries’ Package ‘tseries’\nWang X, Reich N, Horton N (2019) Enriching students’ conceptual understanding of confidence intervals: an interactive trivia-based classroom activity. Am Stat 73(1):50–55\nWong GY, Mason WM (1985) The hierarchical logistic regression model for multilevel analysis. J Am Stat Assoc 80(391):513–524",{"VOID":124},"10.1007\u002Fs00180-022-01287-4","PUBLICATION",[127],"VI","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00180-022-01287-4",[130,146,161],{"id":131,"sortIndex":21,"researcher":20,"roles":132,"affiliations":134,"properties":143,"displayName":145,"givenName":20,"familyName":20},"daee6ba1-5a7a-49fb-8ff0-ec86f9a1bc53",[133],"AUTHOR",[135],{"id":136,"sortIndex":21,"affiliation":137,"properties":20},"67376621-fa08-47e5-a209-e659e1da39c8",{"id":136,"createTime":20,"updateTime":20,"relativeEntities":138,"slug":20,"properties":139,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":142,"statistic":20},[],{"title":140},{"VI":141},"Department of Quality and Production, Instituto Tecnológico Metropolitano (ITM), Medellín, Colombia",[],{"title":144},{"VI":145},"Cristian David Correa-Álvarez",{"id":147,"sortIndex":99,"researcher":20,"roles":148,"affiliations":149,"properties":158,"displayName":160,"givenName":20,"familyName":20},"2985e284-90b3-482e-b556-1a3ca3201bcc",[133],[150],{"id":151,"sortIndex":21,"affiliation":152,"properties":20},"16eacf7f-7b69-4838-b9f1-14bbc2c0f3e2",{"id":151,"createTime":20,"updateTime":20,"relativeEntities":153,"slug":20,"properties":154,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":157,"statistic":20},[],{"title":155},{"VI":156},"School of Statistics, Universidad Nacional de Colombia (Medellín campus), Medellín, Colombia",[],{"title":159},{"VI":160},"Juan Carlos Salazar-Uribe",{"id":162,"sortIndex":100,"researcher":20,"roles":163,"affiliations":164,"properties":173,"displayName":175,"givenName":20,"familyName":20},"022e3c93-e106-4295-b60c-a41afe74e44a",[133],[165],{"id":166,"sortIndex":21,"affiliation":167,"properties":20},"7ac742f4-a75b-4a7d-a39c-e0afc0cb270f",{"id":166,"createTime":20,"updateTime":20,"relativeEntities":168,"slug":20,"properties":169,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":172,"statistic":20},[],{"title":170},{"VI":171},"Department of Mathematics and Center for Biostatistics and Bioinformatics, University of Puerto Rico, Rio Piedras, Puerto Rico",[],{"title":174},{"VI":175},"Luis Raúl Pericchi-Guerra","ARTICLE",{"url":128,"publisher":178,"properties":228},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":179,"slug":10,"properties":180,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":184,"manageAffiliations":197,"indexDatabases":208,"url":94,"thumbnailPath":20,"statistic":223,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":181,"title":182,"eissn":183},{"VOID":13},{"EN":15},{"VOID":17},[185,189,193],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":186,"label":187,"description":188,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":190,"label":191,"description":192,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":194,"label":195,"description":196,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[198,203],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":199,"slug":20,"properties":200,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":202,"statistic":20},[],{"title":201},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":204,"slug":20,"properties":205,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":207,"statistic":20},[],{"title":206},{"EN":55},[49],[209,216],{"id":59,"indexDatabase":210,"url":72,"indexYears":20,"academicFieldIds":215,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":211,"label":212,"description":213,"key":68,"publicationTags":214,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":217,"url":87,"indexYears":88,"academicFieldIds":222,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":218,"label":219,"description":220,"key":84,"publicationTags":221,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":224,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":225,"totalCitation":21,"totalCitationByYear":226,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":227,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"pages":229,"volume":231},{"VOID":230},"1791-1810",{"VOID":232},"38","2022-10-25",2022,[93,70],false,{"id":238,"createTime":239,"updateTime":240,"relativeEntities":241,"slug":242,"properties":243,"entityType":125,"verifyStatus":253,"verifyTime":254,"verifyNote":255,"languages":20,"translateLanguages":256,"viewCount":21,"primaryUrl":257,"fullTextUrl":20,"authors":258,"publicationType":176,"publisherRelationship":296,"citationCount":20,"citationInfo":20,"publishDate":352,"publishYear":353,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":354,"openAccess":20,"references":20,"isForceReanalyzing":236},"4073c66b-4967-481f-a651-3db0fb7108a7","2024-01-19T23:36:03.980+00:00","2026-09-07T03:11:24.650+00:00",[],"Modified-empirical-likelihood-based-confidence-intervals-for-data-containing-many-zero-observations",{"abstract":244,"title":246,"references":249,"doi":251},{"EN":245},"Data containing many zeroes is popular in statistical applications, such as survey data. A confidence interval based on the traditional normal approximation may lead to poor coverage probabilities, especially when the nonzero values are highly skewed and the sample size is small or moderately large. The empirical likelihood (EL), a powerful nonparametric method, was proposed to construct confidence intervals under such a scenario. However, the traditional empirical likelihood experiences the issue of under-coverage problem which causes the coverage probability of the EL-based confidence intervals to be lower than the nominal level, especially in small sample sizes. In this paper, we investigate the numerical performance of three modified versions of the EL: the adjusted empirical likelihood, the transformed empirical likelihood, and the transformed adjusted empirical likelihood for data with various sample sizes and various proportions of zero values. Asymptotic distributions of the likelihood-type statistics have been established as the standard chi-square distribution. Simulations are conducted to compare coverage probabilities with other existing methods under different distributions. Real data has been given to illustrate the procedure of constructing confidence intervals.",{"EN":247,"VI":248},"Modified empirical likelihood-based confidence intervals for data containing many zero observations","Các khoảng tin cậy dựa trên hợp lý thực nghiệm cải biên cho dữ liệu chứa nhiều quan sát mang giá trị không",{"VOID":250},"Bao Y, Vinciotti V, Wit E, ’t Hoen PAC (2014) Joint modeling of ChIP-seq data via a Markov random field model. Biostatistics 15:296–310\nChen J, Chen S-Y, Rao JNK (2003) Empirical likelihood confidence intervals for a population containing many zero values. Can J Stat 31(1):53–68\nChen J, Variyath AM, Abraham B (2008) Adjusted empirical likelihood and its properties. J Comput Graph Stat 17(2):426–443\nChen SX, Qin J (2003) Empirical likelihood-based confidence intrevals for data with possible zero observations. Stat Probab Lett 65:29–37\nCox DR, Snell EJ (1979) On sampling and the estimation of rare errors. Biometrika 66:125–132\nEmerson S, Owen A (2009) Calibration of the empirical likelihood method for a vector mean. Electron J Stat 3:1161–1192\nJing B-Y, Tsao M, Zhou W (2017) Transforming the empirical liklihood towards better accuracy. Can J Stat 45(3):340–352\nKvanli AH, Shen YK, Deng LY (1998) Construction of confidence intervals for the mean of a population contaning many zero values. J Bus Econ Stat 16:362–368\nLiang W, Dai H, He S (2019) Mean empirical likelihood. Computat Stat Data Anal 138:155–169\nLiu Y, Chen J (2010) Adjusted empirical likelihood with high-order precision. Ann Stat 38:1341–1362\nMiao Z, Deng K, Wang X, Zhang X (2018) DEsingle for detecting three types of differential expression in single-cell RNA-seq data. Bioinformatics 34:3223–3224\nOwen AB (1988) Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75:237–249\nOwen AB (1990) Empirical likelihood confidence regions. Ann Stat 18:90–120\nOwen AB (2001) Empirical likelihood. Champan & Hall, New York\nSang J, Wang L, Cao J (2019) Weighted empirical likelihood inference for dynamical correlations. Comput Stat Data Anal 131:194–206\nTamura H (1988) Estimation of rare errors using expert judgement. Biometrika 75:1–9\nTsao M (2013) Extending the empirical likelihood by domain expansion. Can J Stat 41(2):257–274\nWelch A, Zhou XH (2004) Estimating the retransformed mean in a heteroscedastic two-part model. UW Biostatistics Working Paper Series\nZhao P, Rao JNK, Wu C (2020) Bayesian empirical likelihood inference withcomplex survey data. J R Stat Soc Ser B 82:155–174\nZhou XH, Tu W (2000) Confidence intervals for the mean of diagnositic test charge data containing zeros. Biometrics 56:1118–1125",{"VOID":252},"10.1007\u002Fs00180-020-00993-1","VERIFIED","2025-02-02T00:38:32.162+00:00","Auto Verify",[127],"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00180-020-00993-1",[259,274],{"id":260,"sortIndex":21,"researcher":20,"roles":261,"affiliations":262,"properties":271,"displayName":273,"givenName":20,"familyName":20},"8534325f-5853-49c0-97a9-d0c50366f731",[133],[263],{"id":264,"sortIndex":21,"affiliation":265,"properties":20},"bc591f7d-6a45-4abf-a051-3a0d5a34587d",{"id":264,"createTime":20,"updateTime":20,"relativeEntities":266,"slug":20,"properties":267,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":270,"statistic":20},[],{"title":268},{"VI":269},"Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, USA",[],{"title":272},{"VI":273},"Patrick Stewart",{"id":275,"sortIndex":99,"researcher":20,"roles":276,"affiliations":277,"properties":293,"displayName":295,"givenName":20,"familyName":20},"781f6826-6c54-46b8-a72f-7e16f93397b1",[133],[278,284],{"id":264,"sortIndex":21,"affiliation":279,"properties":20},{"id":264,"createTime":20,"updateTime":20,"relativeEntities":280,"slug":20,"properties":281,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":283,"statistic":20},[],{"title":282},{"VI":269},[],{"id":285,"sortIndex":99,"affiliation":286,"properties":292},"28a43c4a-05e2-425a-97bd-e9c03f6bd3cf",{"id":285,"createTime":20,"updateTime":20,"relativeEntities":287,"slug":20,"properties":288,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":291,"statistic":20},[],{"title":289},{"VI":290},"School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China",[],{},{"title":294},{"VI":295},"Wei Ning",{"url":257,"publisher":297,"properties":347},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":298,"slug":10,"properties":299,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":303,"manageAffiliations":316,"indexDatabases":327,"url":94,"thumbnailPath":20,"statistic":342,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":300,"title":301,"eissn":302},{"VOID":13},{"EN":15},{"VOID":17},[304,308,312],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":305,"label":306,"description":307,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":309,"label":310,"description":311,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":313,"label":314,"description":315,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[317,322],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":318,"slug":20,"properties":319,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":321,"statistic":20},[],{"title":320},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":323,"slug":20,"properties":324,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":326,"statistic":20},[],{"title":325},{"EN":55},[49],[328,335],{"id":59,"indexDatabase":329,"url":72,"indexYears":20,"academicFieldIds":334,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":330,"label":331,"description":332,"key":68,"publicationTags":333,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":336,"url":87,"indexYears":88,"academicFieldIds":341,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":337,"label":338,"description":339,"key":84,"publicationTags":340,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":343,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":344,"totalCitation":21,"totalCitationByYear":345,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":346,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"pages":348,"volume":350},{"VOID":349},"2019-2042",{"VOID":351},"35","2020-04-30",2020,[93,70],{"id":356,"createTime":357,"updateTime":358,"relativeEntities":359,"slug":360,"properties":361,"entityType":125,"verifyStatus":253,"verifyTime":372,"verifyNote":255,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":373,"fullTextUrl":20,"authors":374,"publicationType":176,"publisherRelationship":443,"citationCount":20,"citationInfo":20,"publishDate":499,"publishYear":500,"citationAnalyzeStatus":501,"lastCitationAnalyze":502,"indexDatabases":503,"openAccess":20,"references":20,"isForceReanalyzing":236},"fe89bcb4-356c-4460-8183-b9b6fa060bac","2024-01-18T17:39:11.081+00:00","2026-08-19T19:27:13.024+00:00",[],"Testing-the-equality-of-several-linear-regression-models",{"abstract":362,"title":364,"gsPaper":366,"references":368,"doi":370},{"EN":363},"The linear regression models are widely used in different research fields, and often there is the need to analyze if there are similarities between two or more different linear models or to verify if a given relation between two variables remains the same in different intervals of time, in particular in cases where small differences might make a big difference. Motivated by these problems the authors consider a test of equality of k linear regression models which is a simultaneous test of equality of slopes, intercepts and variances. In order to overcome the extreme difficulties that exist in the use of the exact distribution of the likelihood ratio test (LRT) statistic and to make this test reliable and easy to use, we propose the use of near-exact distributions to approximate the distribution of the LRT statistic, under \n                  \n                    \n                  \n                  $$H_0$$\n                  \n                    \n                  \n                , in the balanced case, and of new asymptotic approximations for the unbalanced case. The near-exact approximations are built by approximating one factor of an adequate factorization of the characteristic function of the logarithm of the LRT statistic and may be easily implemented. The asymptotic approximations are developed using an expansion for the ratio of gamma functions. The quality of these approximations is analyzed and confirmed. Power studies are conducted in order to better assess the performance of the test. Finally to illustrate the applicability of the test we consider a real data set of gross domestic product at market prices and final consumption expenditure in European countries and one tests the existence of similarities between countries.",{"EN":365},"Testing the equality of several linear regression models",{"VOID":367},"[]",{"VOID":369},"Azaisa J-M, Delmas C, Rabier C-E (2014) Likelihood ratio test process for quantitative trait locus detection. Statistics 48:787–801\nBox GEP (1949) A general distribution theory for a class of likelihood criteria. Biometrika 36:317–346\nBretz F, Hothorn T, Westfall P (2008) Multiple comparison procedures in linear models. In: Brito P (ed) COMPSTAT 2008. Proceedings in computational statistics. Physica-Verlag, Heidelberg\nChen HJ, Yuan SP (1982) On concurrence of several linear regressions and applications to problems of enzyme kinetics. Statistics 11:395–409\nCoelho CA (1998) The generalized integer Gamma distribution—a basis for distributions in multivariate statistics. J Multivar Anal 64:86–102\nCoelho CA (2004) The generalized near-integer gamma distribution: a basis for ‘near-exact’ approximations to the distribution of statistics which are the product of an odd number of independent beta random variables. J Multivar Anal 89:191–218\nCoelho CA, Marques FJ (2009) The advantage of decomposing elaborate hypotheses on covariance matrices into conditionally independent hypotheses in building near-exact distributions for the test statistics. Linear Algebra Appl 430:2592–2606\nCoelho CA, Marques FJ (2010) Near-exact distributions for the independence and sphericity likelihood ratio test statistics. J Multivar Anal 101:583–593\nCoelho CA, Arnold BC, Marques FJ (2010) Near-exact distributions for certain likelihood ratio test statistics. J Stat Theory Pract 4:711–725\nCoelho CA, Alberto RP (2012) On the distribution of the product of independent beta random variables applications. Technical report, CMA 12\nDeaton A (1992) Understanding consumption. Oxford University Press, Oxford\nJohansen S (2000) A Bartlett correction factor for tests on the cointegrating relations. Econom Theory 16:740–778\nKrugman PR, Obstfeld M, Melitz MJ (2012) International economics: theory and policy. Pearson, Harlow\nLuke YL (1969) The special functions and their approximations. Academic Press Inc, London\nMarques FJ, Coelho CA (2013) Obtaining the exact and near-exact distributions of the likelihood ratio statistic to test circular symmetry through the use of characteristic functions. Comput Stat 28:2091–2115\nMarques FJ, Coelho CA, Arnold BC (2010) A general near-exact distribution theory for the most common likelihood ratio test statistics used in multivariate analysis. Test 20:180–203\nMoschopoulos PG (1986) New representations for the distribution function of a class of likelihood ratio criteria. J Stat Res 20(1&2):13–20\nMoschopoulos PG (1989) Tests of hypotheses on concomitant variables in linear models. Commun Stat Theory Methods 18(5):1735–1746\nNielsen HB, Rahbek A (2007) The likelihood ratio test for cointegration ranks in the I(2) model. Econom Theory 23:615–637\nNkurunziza S (2008) Likelihood ratio test for a special predator–prey system. Statistics 42:149–166\nPark J, Sinha B, Shah A, Xu D, Lin J (2015) Likelihood ratio tests for interval hypotheses with applications. Commun Stat 44(11):2351–2370\nPaternoster R, Brame R, Mazerolle P, Piquero A (1998) Using the correct statistical test for the equality of regression coefficients. Criminology 36:859–866\nSolomon H, Stephens MA (1978) Approximations to density functions using Pearson curves. J Am Stat Assoc 73:153–160\nStöckl D, Dewitte K, Thienpont LM (1998) Validity of linear regression in method comparison studies: is it limited by the statistical model or the quality of the analytical input data? Clin Chem 44:2340–2346\nTricomi FG, Erdélyi A (1951) The asymptotic expansion of a ratio of gamma functions. Pac J Math 1:133–142\nWilks SS (1938) The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann Math Stat 9:60–62",{"VOID":371},"10.1007\u002Fs00180-016-0703-1","2024-08-31T14:19:23.657+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00180-016-0703-1",[375,399,419],{"id":376,"sortIndex":21,"researcher":20,"roles":377,"affiliations":378,"properties":396,"displayName":398,"givenName":20,"familyName":20},"ba83852a-43d5-4ff5-b1f0-6113c12b1758",[133],[379,387],{"id":380,"sortIndex":21,"affiliation":381,"properties":20},"96175e84-24de-4320-a4aa-136693f81db8",{"id":380,"createTime":20,"updateTime":20,"relativeEntities":382,"slug":20,"properties":383,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":386,"statistic":20},[],{"title":384},{"VI":385},"Center for Mathematics and Applications (CMA), NOVA University of Lisbon, Lisbon, Portugal",[],{"id":388,"sortIndex":99,"affiliation":389,"properties":395},"af383a76-333d-4e3c-8e69-3ff6fa5c7c9f",{"id":388,"createTime":20,"updateTime":20,"relativeEntities":390,"slug":20,"properties":391,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":394,"statistic":20},[],{"title":392},{"VI":393},"Departamento de Matemática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal",[],{},{"title":397},{"VI":398},"Filipe J. Marques",{"id":400,"sortIndex":99,"researcher":20,"roles":401,"affiliations":402,"properties":416,"displayName":418,"givenName":20,"familyName":20},"1f405c11-8237-4b88-8f27-d0c356a8cfe2",[133],[403,409],{"id":380,"sortIndex":21,"affiliation":404,"properties":20},{"id":380,"createTime":20,"updateTime":20,"relativeEntities":405,"slug":20,"properties":406,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":408,"statistic":20},[],{"title":407},{"VI":385},[],{"id":388,"sortIndex":99,"affiliation":410,"properties":415},{"id":388,"createTime":20,"updateTime":20,"relativeEntities":411,"slug":20,"properties":412,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":414,"statistic":20},[],{"title":413},{"VI":393},[],{},{"title":417},{"VI":418},"Carlos A. Coelho",{"id":420,"sortIndex":100,"researcher":20,"roles":421,"affiliations":422,"properties":440,"displayName":442,"givenName":20,"familyName":20},"58d072b7-76fb-4574-9ad9-14f1f0905e24",[133],[423,431],{"id":424,"sortIndex":21,"affiliation":425,"properties":20},"0008c4bc-e5cf-47de-9e7a-b64f9b8417f6",{"id":424,"createTime":20,"updateTime":20,"relativeEntities":426,"slug":20,"properties":427,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":430,"statistic":20},[],{"title":428},{"VI":429},"Federal University of Bahia, Salvador, Brazil",[],{"id":432,"sortIndex":99,"affiliation":433,"properties":439},"4f0ef1bb-b9f7-419d-a30a-a83dafbc1b37",{"id":432,"createTime":20,"updateTime":20,"relativeEntities":434,"slug":20,"properties":435,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":438,"statistic":20},[],{"title":436},{"VI":437},"CAST, University of Tampere, Tampere, Finland",[],{},{"title":441},{"VI":442},"Paulo C. Rodrigues",{"url":373,"publisher":444,"properties":494},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":445,"slug":10,"properties":446,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":450,"manageAffiliations":463,"indexDatabases":474,"url":94,"thumbnailPath":20,"statistic":489,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":447,"title":448,"eissn":449},{"VOID":13},{"EN":15},{"VOID":17},[451,455,459],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":452,"label":453,"description":454,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":456,"label":457,"description":458,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":460,"label":461,"description":462,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[464,469],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":465,"slug":20,"properties":466,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":468,"statistic":20},[],{"title":467},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":470,"slug":20,"properties":471,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":473,"statistic":20},[],{"title":472},{"EN":55},[49],[475,482],{"id":59,"indexDatabase":476,"url":72,"indexYears":20,"academicFieldIds":481,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":477,"label":478,"description":479,"key":68,"publicationTags":480,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":483,"url":87,"indexYears":88,"academicFieldIds":488,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":484,"label":485,"description":486,"key":84,"publicationTags":487,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":490,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":491,"totalCitation":21,"totalCitationByYear":492,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":493,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"pages":495,"volume":497},{"VOID":496},"1453-1480",{"VOID":498},"32","2016-12-01",2016,"ERROR_IN_GET_PLATFORM_ID","2026-08-19T19:27:13.022+00:00",[93,70],{"id":505,"createTime":506,"updateTime":507,"relativeEntities":508,"slug":509,"properties":510,"entityType":125,"verifyStatus":253,"verifyTime":520,"verifyNote":255,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":521,"fullTextUrl":20,"authors":522,"publicationType":176,"publisherRelationship":577,"citationCount":20,"citationInfo":20,"publishDate":632,"publishYear":353,"citationAnalyzeStatus":501,"lastCitationAnalyze":633,"indexDatabases":634,"openAccess":20,"references":20,"isForceReanalyzing":236},"53d8681c-360e-4d81-8167-401a47ce0488","2023-12-12T23:32:19.312+00:00","2026-08-15T15:04:28.579+00:00",[],"Hierarchical-inference-for-genome-wide-association-studies-a-view-on-methodology-with-software",{"abstract":511,"title":513,"gsPaper":515,"references":516,"doi":518},{"EN":512},"We provide a view on high-dimensional statistical inference for genome-wide association studies. It is in part a review but covers also new developments for meta analysis with multiple studies and novel software in terms of an R-package hierinf. Inference and assessment of significance is based on very high-dimensional multivariate (generalized) linear models: in contrast to often used marginal approaches, this provides a step towards more causal-oriented inference.",{"EN":514},"Hierarchical inference for genome-wide association studies: a view on methodology with software",{"VOID":367},{"VOID":517},"Alexander D, Lange K (2011) Stability selection for genome-wide association. Genet Epidemiol 35:722–728\nBaierl A, Bogdan M, Frommlet F n, Futschik A (2006) On locating multiple interacting quantitative trait loci in int ercross designs. Genetics 173:1693–1703\nBenjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300\nBenjamini Y, Yekutieli D (2005) Quantitative trait loci analysis using the false discovery rate. Genetics 171:783–790\nBickel P, Ritov Y, Tsybakov A (2009) Simultaneous analysis of Lasso and Dantzig selector. Ann Stat 37:1705–1732\nBrzyski D, Peterson CB, Sobczyk P, Candès EJ, Bogdan M, Sabatti C (2017) Controlling the rate of GWAS false discoveries. Genetics 205:61–75\nBühlmann P (2013) Statistical significance in high-dimensional linear models. Bernoulli 19:1212–1242\nBühlmann P (2017) High-dimensional statistics, with applications to genome-wide association studies. EMS Surv Math Sci 4:45–75\nBühlmann P, Mandozzi J (2014) High-dimensional variable screening and bias in subsequent inference, with an empirical comparison. Comput Stat 29:407–430\nBühlmann P, Rütimann P, van de Geer S, Zhang C-H (2013) Correlated variables in regression: clustering and sparse estimation. J Stat Plan Inference 143:1835–1858\nBühlmann P, van de Geer S (2011) Statistics for high-dimensional data: methods, theory and applications. Springer, New York\nBühlmann P, van de Geer S (2015) High-dimensional inference in misspecified linear models. Electron J Stat 9:1449–1473\nBuja A, Berk R, Brown L, George E, Pitkin E, Traskin M, Zhan K, Zhao L (2014) Models as approximations, part I: a conspiracy of nonlinearity and random regressors in linear regression. Preprint arXiv:1404.1578\nBush WS, Moore JH (2012) Genome-wide association studies. PLOS Comput Biol 8:e1002822\nBuzdugan L (2019) hierGWAS: assessing statistical significance in predictive GWA studies. R package version 1.17.0. https:\u002F\u002Fwww.bioconductor.org\u002Fpackages\u002Fdevel\u002Fbioc\u002Fhtml\u002FhierGWAS.html\nBuzdugan L, Kalisch M, Navarro A, Schunk D, Fehr E, Bühlmann P (2016) Assessing statistical significance in multivariable genome wide association analysis. Bioinformatics 32:1990–2000\nCantor RM, Lange K, Sinsheimer JS (2010) Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 86:6–22\nCarbonetto P, Stephens M (2012) Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Anal 7:73–108\nChatterjee A, Lahiri S (2011) Bootstrapping Lasso estimators. J Am Stat Assoc 106:608–625\nChatterjee A, Lahiri S (2013) Rates of convergence of the adaptive LASSO estimators to the oracle distribution and higher order refinements by the bootstrap. Ann Stat 41:1232–1259\nDezeure R, Bühlmann P, Meier L, Meinshausen N (2015) High-dimensional inference: confidence intervals, p-values and R-software hdi. Stat Sci 30:533–558\nDezeure R, Bühlmann P, Zhang C-H (2017) High-dimensional simultaneous inference with the bootstrap (with discussion). TEST 26:685–719\nDolejsi E, Bodenstorfer B, Frommlet F (2014) Analyzing genome-wide association studies with an FDR controlling modification of the bayesian information criterion. PloS One 9(7):e103322\nFriedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1):1–22\nFrommlet F, Bogdan M, Ramsey D (2016) Phenotypes and genotypes: the search for influential genes. Springer, New York\nFrommlet F, Ruhaltinger F, Twaróg P, Bogdan M (2012) Modified versions of Bayesian information criterion for genome-wide association studies. Comput Stat Data Anal 56(5):1038–1051\nGoeman JJ, Finos L (2012) The inheritance procedure: multiple testing of tree-structured hypotheses. Stat Appl Genet Mol Biol 11:1–18\nGoeman JJ, Solari A (2010) The sequential rejection principle of familywise error control. Ann Stat 38:3782–3810\nGoeman JJ, Solari A (2011) Multiple testing for exploratory research. Stat Sci 26:584–597\nHartigan J (1975) Clustering algorithms. Wiley, New York\nHe Q, Lin D-Y (2011) A variable selection method for genome-wide association studies. Bioinformatics 27:1–8\nHeller R, Chatterjee N, Krieger A, Shi J, (2017) Post-selection inference following aggregate level hypothesis testing in large scale genomic data. J Am Stat Assoc. https:\u002F\u002Fdoi.org\u002F10.1080\u002F01621459.2017.1375933\nHoggart CJ, Whittaker JC, De Iorio M, Balding DJ (2008) Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLOS Genet 4:e1000130\nJavanmard A, Montanari A (2014) Confidence intervals and hypothesis testing for high-dimensional regression. J Mach Learn Res 15:2869–2909\nKlasen J, Barbez E, Meier L, Meinshausen N, Bühlmann P, Koornneef M, Busch W, Schneeberger K (2016) A multi-marker association method for genome-wide association studies without the need for population structure correction. Nat Commun 7:Article number 13299. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fncomms13299\nLi J, Das K, Fu G, Li R, Wu R (2011) The Bayesian Lasso for genome-wide association studies. Bioinformatics 27:516–523\nLippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D (2011) Fast linear mixed models for genome-wide association studies. Nat Methods 8:833\nLiu H, Yu B (2013) Asymptotic properties of Lasso + mLS and Lasso + Ridge in sparse high-dimensional linear regression. Electron J Stat 7:3124–3169\nLu Y, Dhillon P, Foster DP, Ungar L (2013) Faster ridge regression via the subsampled randomized hadamard transform. In: Advances in neural information processing systems, vol 26, pp 369–377\nMalo N, Libiger O, Schork N (2008) Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am J Hum Genet 82:375–385\nMandozzi J, Bühlmann P (2016a) Hierarchical testing in the high-dimensional setting with correlated variables. J Am Stat Assoc 111:331–343\nMandozzi J, Bühlmann P (2016b) A sequential rejection testing method for high-dimensional regression with correlated variables. Int J Biostat 12:79–95\nMeijer RJ, Krebs TJ, Goeman JJ (2015) A region-based multiple testing method for hypotheses ordered in space or time. Stat Appl Genet Mol Biol 14:1–19\nMeinshausen N (2008) Hierarchical testing of variable importance. Biometrika 95:265–278\nMeinshausen N, Bühlmann P (2006) High-dimensional graphs and variable selection with the Lasso. Ann Stat 34:1436–1462\nMeinshausen N, Bühlmann P (2010) Stability selection (with discussion). J R Stat Soc Ser B 72:417–473\nMeinshausen N, Meier L, Bühlmann P (2009) P-values for high-dimensional regression. J Am Stat Assoc 104:1671–1681\nNagelkerke NJ et al (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692\nNovembre J, Johnson T, Bryc K, Kutalik Z, Boyko RA, Auton A, Indap A, King K, Bergmann S, Nelson M, Stephens M, Bustamante C (2008) Genes mirror geography within Europe. Nature 456:98–101\nPearl J (2000) Causality: models, reasoning and inference. Cambridge University Press, Cambridge\nPeterson CB, Bogomolov M, Benjamini Y, Sabatti C (2016) Many phenotypes without many false discoveries: error controlling strategies for multitrait association studies. Genet Epidemiol 40:45–56\nPilanci M, Wainwright MJ (2015) Randomized sketches of convex programs with sharp guarantees. IEEE Trans Inf Theory 61:5096–5115\nPlagnol V, Howson JM, Smyth DJ, Walker N, Hafler JP, Wallace C, Stevens H, Jackson L, Simmonds MJ, Bingley PJ et al (2011) Genome-wide association analysis of autoantibody positivity in type 1 diabetes cases. PLOS Genet 7:e1002216\nR Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https:\u002F\u002Fwww.R-project.org\u002F\nRakitsch B, Lippert C, Stegle O, Borgwardt K (2013) A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics 29:206–214\nSabatti C, Freimer N et al (2003) False discovery rate in linkage and association genome screens for complex disorders. Genetics 164:829–833\nScott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding C-J, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li X-Y, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M (2007) A genome-wide association study of type 2 diabetes in finns detects multiple susceptibility variants. Science 316:1341–1345\nShah R, Bühlmann P (2018) Goodness of fit tests for high-dimensional linear models. J R Stat Soc Ser B 80:113–135\nShah R, Samworth R (2013) Variable selection with error control: another look at stability selection. J R Stat Soc Ser B 75:55–80\nShao J, Deng X (2012) Estimation in high-dimensional linear models with deterministic design matrices. Ann Stat 40:812–831\nShi G, Boerwinkle E, Morrison AC, Gu CC, Chakravarti A, Rao D (2011) Mining gold dust under the genome wide significance level: a two-stage approach to analysis of GWAS. Genet Epidemiol 35:111–118\nStekhoven DJ, Bühlmann P (2012) Missforest - non-parametric missing value imputation for mixed-type data. Bioinformatics 28(1):112–118\nStorey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci 100:9440–9445\nStouffer SA, Suchman EA, DeVinney LC, Star SA, Williams RM Jr (1949) The American soldier: adjustment during army life. (Studies in social psychology in World War II), vol 1. Princeton University Press, Princeton\nSur P, Candès E (2019) A modern maximum-likelihood theory for high-dimensional logistic regression. Proc Nat Acad Sci 116(29):14516–14525\nThe Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678\nTibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B 58:267–288\nTippett LHC (1931) Methods of statistics, 1st edn. Williams Norgate, London\nvan Buuren S, Groothuis-Oudshoorn K (2011) mice: Multivariate imputation by chained equations in R. J Stat Soft Articles 45:1–67\nvan de Geer S (2007) The deterministic Lasso. In: JSM proceedings, 2007, 140. American Statistical Association\nvan de Geer S (2016) Estimation and testing under sparsity: École d’Été de Probabilités des Saint-Flour XLV–2015. Lecture Notes in Mathematics, vol 2159. Springer, New York\nvan de Geer S, Bühlmann P, Ritov Y, Dezeure R (2014) On asymptotically optimal confidence regions and tests for high-dimensional models. Ann Stat 42:1166–1202\nWasserman L, Roeder K (2009) High dimensional variable selection. Ann Stat 37:2178–2201\nWu J, Devlin B, Ringquist S, Trucco M, Roeder K (2010a) Screen and clean: a tool for identifying interactions in genome-wide association studies. Genet Epidemiol 34:275–285\nWu M, Kraft P, Epstein M, Taylor D, Chanock S, Hunter D, Lin X (2010b) Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet 86:929–942\nZeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JRB, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney ASF, McCarthy MI, Hattersley AT (2007) Replication of genome-wide association signals in uk samples reveals risk loci for type 2 diabetes. Science 316:1336–1341\nZhang C-H, Zhang S (2014) Confidence intervals for low dimensional parameters in high dimensional linear models. J R Stat Soc Ser B 76:217–242\nZhao P, Yu B (2006) On model selection consistency of Lasso. J Mach Learn Res 7:2541–2563\nZhou X, Carbonetto P, Stephens M (2013) Bayesian sparse linear mixed models. PLOS Genet 9:e1003264\nZhou X, Stephens M (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11:407–409\nZou H (2006) The adaptive Lasso and its oracle properties. J Am Stat Assoc 101:1418–1429",{"VOID":519},"10.1007\u002Fs00180-019-00939-2","2024-09-13T05:40:34.318+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs00180-019-00939-2",[523,538,551,564],{"id":524,"sortIndex":21,"researcher":20,"roles":525,"affiliations":526,"properties":535,"displayName":537,"givenName":20,"familyName":20},"e00016b0-7db5-483f-a259-2224dbe6a87c",[133],[527],{"id":528,"sortIndex":21,"affiliation":529,"properties":20},"19fb3f8f-9ee9-4da0-a539-734e0a5cebe3",{"id":528,"createTime":20,"updateTime":20,"relativeEntities":530,"slug":20,"properties":531,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":534,"statistic":20},[],{"title":532},{"VI":533},"Seminar for Statistics, ETH Zürich, Zürich, Switzerland",[],{"title":536},{"VI":537},"Claude Renaux",{"id":539,"sortIndex":99,"researcher":20,"roles":540,"affiliations":541,"properties":548,"displayName":550,"givenName":20,"familyName":20},"f386d06e-919b-4a47-a659-4e48792a8b06",[133],[542],{"id":528,"sortIndex":21,"affiliation":543,"properties":20},{"id":528,"createTime":20,"updateTime":20,"relativeEntities":544,"slug":20,"properties":545,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":547,"statistic":20},[],{"title":546},{"VI":533},[],{"title":549},{"VI":550},"Laura Buzdugan",{"id":552,"sortIndex":100,"researcher":20,"roles":553,"affiliations":554,"properties":561,"displayName":563,"givenName":20,"familyName":20},"1e746afb-25a4-46ac-a948-5543f5a17c93",[133],[555],{"id":528,"sortIndex":21,"affiliation":556,"properties":20},{"id":528,"createTime":20,"updateTime":20,"relativeEntities":557,"slug":20,"properties":558,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":560,"statistic":20},[],{"title":559},{"VI":533},[],{"title":562},{"VI":563},"Markus Kalisch",{"id":565,"sortIndex":101,"researcher":20,"roles":566,"affiliations":567,"properties":574,"displayName":576,"givenName":20,"familyName":20},"22c58dd6-ca4e-4d53-9894-ae2545d8c6fb",[133],[568],{"id":528,"sortIndex":21,"affiliation":569,"properties":20},{"id":528,"createTime":20,"updateTime":20,"relativeEntities":570,"slug":20,"properties":571,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":573,"statistic":20},[],{"title":572},{"VI":533},[],{"title":575},{"VI":576},"Peter Bühlmann",{"url":521,"publisher":578,"properties":628},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":579,"slug":10,"properties":580,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":584,"manageAffiliations":597,"indexDatabases":608,"url":94,"thumbnailPath":20,"statistic":623,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":581,"title":582,"eissn":583},{"VOID":13},{"EN":15},{"VOID":17},[585,589,593],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":586,"label":587,"description":588,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":590,"label":591,"description":592,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":594,"label":595,"description":596,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[598,603],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":599,"slug":20,"properties":600,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":602,"statistic":20},[],{"title":601},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":604,"slug":20,"properties":605,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":607,"statistic":20},[],{"title":606},{"EN":55},[49],[609,616],{"id":59,"indexDatabase":610,"url":72,"indexYears":20,"academicFieldIds":615,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":611,"label":612,"description":613,"key":68,"publicationTags":614,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":617,"url":87,"indexYears":88,"academicFieldIds":622,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":618,"label":619,"description":620,"key":84,"publicationTags":621,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":624,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":625,"totalCitation":21,"totalCitationByYear":626,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":627,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"pages":629,"volume":631},{"VOID":630},"1-40",{"VOID":351},"2020-01-06","2026-08-15T15:04:28.578+00:00",[93,70],{"id":636,"createTime":637,"updateTime":638,"relativeEntities":639,"slug":640,"properties":641,"entityType":125,"verifyStatus":253,"verifyTime":650,"verifyNote":255,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":651,"fullTextUrl":20,"authors":652,"publicationType":176,"publisherRelationship":704,"citationCount":760,"citationInfo":761,"publishDate":764,"publishYear":762,"citationAnalyzeStatus":765,"lastCitationAnalyze":766,"indexDatabases":767,"openAccess":20,"references":20,"isForceReanalyzing":236},"c82e0126-8752-4d33-80c8-e1bde968b3ba","2024-01-10T10:17:22.436+00:00","2026-07-31T05:59:03.153+00:00",[],"The-2013-Data-Expo-of-the-American-Statistical-Association",{"title":642,"gsPaper":644,"references":646,"doi":648},{"EN":643},"The 2013 Data Expo of the American Statistical Association",{"VOID":645},"[\"39865886786461824\"]",{"VOID":647},"Ackerman S (2019) Consistency of survey opinions and external data. Comput Stat 34(4). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00180-019-00882-2\nCook D (2014) The 2011 Data Expo of the American Statistical Association. Comput Stat 29(1–2):117–119\nda Silva N, Alvarez I (2019) Clicks and cliques: Exploring the soul of the community. Comput Stat 34(4). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00180-019-00881-3\nGandrud C (2015) Reproducible research with R and RStudio, 2nd edn. Chapman and Hall\u002FCRC, Boca Raton\nKaplan A, Hare E (2019) Putting down roots: a graphical exploration of community attachment. Comput Stat 34(4). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00180-018-0850-7\nMaurer K, Osthus D, Loy A (2019) A tale of four cities: Exploring the soul of State College, Detroit. Milledgeville and Biloxi. Comput Stat 34(4). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00180-018-00863-x\nMcNamara A (2019) Community engagement and subgroup meta-knowledge: some factors in the soul of a community. Comput Stat 34(4). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00180-019-00879-x\nMurrell P (2010) The 2006 Data Expo of the American Statistical Association. Comput Stat 25(4):551–554\nOrth JM (2013) Dynamic graphics: an interactive analysis of what attaches people to their communities. In: 2013 JSM Proceedings, American Statistical Association, Alexandria, VA, pp 3013–3025\nOrth JM (2019) Drivers of community attachment: an interactive analysis. Comput Stat 34(4). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00180-018-00862-y\nQuach A, Symanzik J, Forsgren Velasquez N (2013) Soul of the community: a first attempt to assess attachment to a community. In: 2013 JSM Proceedings, American Statistical Association, Alexandria, VA, pp 4053–4067\nQuach A, Symanzik J, Forsgren N (2019) Soul of the community: an attempt to assess attachment to a community. Comput Stat 34(4). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00180-019-00866-2\nStodden V, Leisch F, Peng RD (eds) (2014) Implementing reproducible research. Chapman and Hall\u002FCRC, Boca Raton\nWickham H (2011) ASA 2009 data expo. J Comput Graph Stat 20(2):281–283\nXie Y (2014) knitr: a comprehensive tool for reproducible research in R. In: Stodden V, Leisch F, Peng RD (eds) Implementing reproducible research. Chapman and Hall\u002FCRC, Boca Raton, pp 3–31\nXie Y (2015) Dynamic documents with R and knitr, 2nd edn. Chapman and Hall\u002FCRC, Boca Raton\nXie Y (2016) knitr: a general-purpose package for dynamic report generation in R. http:\u002F\u002Fyihui.name\u002Fknitr\u002F, R package version 1.15",{"VOID":649},"10.1007\u002Fs00180-019-00923-w","2024-05-11T20:58:25.145+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00180-019-00923-w",[653,672,687],{"id":654,"sortIndex":21,"researcher":20,"roles":655,"affiliations":656,"properties":667,"displayName":669,"givenName":20,"familyName":20},"e328d176-41e4-4d77-992b-380c5f5af459",[133],[657],{"id":658,"sortIndex":21,"affiliation":659,"properties":665},"54a61b9d-a027-4150-9655-8f098a3847df",{"id":658,"createTime":20,"updateTime":20,"relativeEntities":660,"slug":20,"properties":661,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":664,"statistic":20},[],{"title":662},{"VI":663},"Department of Statistics, Iowa State University, Ames, USA",[],{"title":666},{"VI":663},{"title":668,"gsAuthor":670},{"VI":669},"Heike Hofmann",{"VOID":671},"[\"JetXiq4AAAAJ\"]",{"id":673,"sortIndex":99,"researcher":20,"roles":674,"affiliations":675,"properties":684,"displayName":686,"givenName":20,"familyName":20},"3513cf04-d76c-422c-8d83-f9034fc06aa0",[133],[676],{"id":677,"sortIndex":21,"affiliation":678,"properties":20},"8cb8a696-9b2b-4fd0-a51c-169dab633bbb",{"id":677,"createTime":20,"updateTime":20,"relativeEntities":679,"slug":20,"properties":680,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":683,"statistic":20},[],{"title":681},{"VI":682},"RStudio, Inc., Boston, USA",[],{"title":685},{"VI":686},"Hadley Wickham",{"id":688,"sortIndex":100,"researcher":20,"roles":689,"affiliations":690,"properties":699,"displayName":701,"givenName":20,"familyName":20},"a2f55efb-60cc-4d26-8dc1-07c661260907",[133],[691],{"id":692,"sortIndex":21,"affiliation":693,"properties":20},"7821a173-834c-4135-b9f7-c3d92cb97507",{"id":692,"createTime":20,"updateTime":20,"relativeEntities":694,"slug":20,"properties":695,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":698,"statistic":20},[],{"title":696},{"VI":697},"Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia",[],{"title":700,"gsAuthor":702},{"VI":701},"Dianne Cook",{"VOID":703},"[\"_b0uchgAAAAJ\"]",{"url":651,"publisher":705,"properties":755},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":706,"slug":10,"properties":707,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":711,"manageAffiliations":724,"indexDatabases":735,"url":94,"thumbnailPath":20,"statistic":750,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":708,"title":709,"eissn":710},{"VOID":13},{"EN":15},{"VOID":17},[712,716,720],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":713,"label":714,"description":715,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":717,"label":718,"description":719,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":721,"label":722,"description":723,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[725,730],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":726,"slug":20,"properties":727,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":729,"statistic":20},[],{"title":728},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":731,"slug":20,"properties":732,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":734,"statistic":20},[],{"title":733},{"EN":55},[49],[736,743],{"id":59,"indexDatabase":737,"url":72,"indexYears":20,"academicFieldIds":742,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":738,"label":739,"description":740,"key":68,"publicationTags":741,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":744,"url":87,"indexYears":88,"academicFieldIds":749,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":745,"label":746,"description":747,"key":84,"publicationTags":748,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":751,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":752,"totalCitation":21,"totalCitationByYear":753,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":754,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"pages":756,"volume":758},{"VOID":757},"1443-1447",{"VOID":759},"34",5,{"total":760,"publishYear":762,"statisticByYear":763},2019,{},"2019-10-18","ERROR_IN_ANALYZE_CITATION","2026-07-31T05:59:03.151+00:00",[93,70],{"id":769,"createTime":770,"updateTime":771,"relativeEntities":772,"slug":773,"properties":774,"entityType":125,"verifyStatus":253,"verifyTime":787,"verifyNote":255,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":788,"fullTextUrl":20,"authors":789,"publicationType":176,"publisherRelationship":822,"citationCount":21,"citationInfo":873,"publishDate":875,"publishYear":500,"citationAnalyzeStatus":19,"lastCitationAnalyze":876,"indexDatabases":877,"openAccess":20,"references":20,"isForceReanalyzing":236},"b9a20cfd-c031-4240-8039-e8fedf5b6d03","2024-04-08T18:09:12.225+00:00","2026-07-30T16:28:44.845+00:00",[],"Prediction-of-censored-exponential-lifetimes-in-a-simple-step-stress-model-under-progressive-Type-II-censoring",{"abstract":775,"title":777,"gsPaper":779,"keywords":781,"references":783,"doi":785},{"EN":776},"In this article, we consider the problem of predicting survival times of units from the exponential distribution which are censored under a simple step-stress testing experiment. Progressive Type-II censoring are considered for the form of censoring. Two kinds of predictors—the maximum likelihood predictors (MLP) and the conditional median predictors (CMP)—are derived. Some numerical examples are presented to illustrate the prediction methods developed here. Using simulation studies, prediction intervals are generated for these examples. We then compare the MLP and the CMP with respect to mean squared prediction error and the prediction interval.",{"EN":778},"Prediction of censored exponential lifetimes in a simple step-stress model under progressive Type II censoring",{"VOID":780},"[\"14526031012424677223\"]",{"EN":782},"",{"VOID":784},"Balakrishnan N (2007) Progressive censoring methodology: an appraisal (with discussions). Test 16:211–296\nBalakrishnan N (2009) A synthesis of exact inferential results for exponential step-stress models and associated optimal accelerated life-tests. Metrika 69:351–396\nBalakrishnan N, Cohen AC (1991) Order statistics and inference: estimation methods. Academic Press, San Diego\nBalakrishnan N, Cramer E (2014) The art of progressive censoring. Birkhauser, Boston\nBalakrishnan N, Han D (2008) Exact inference for a simple step-stress model with competing risks for failure from exponential distribution under Type-II censoring. J Stat Plan Inference 138:4172–4186\nBalakrishnan N, Kundu D, Ng HKT, Kannan N (2007) Point and interval estimation for a simple step-stress model with Type-II censoring. J Qual Technol 9:35–47\nBai DS, Kim MS, Lee SH (1989) Optimum simple step-stress accelerated life test with censoring. IEEE Trans Reliab 38:528–532\nBasak P, Balakrishnan N (2003) Maximum likelihood prediction of future record statistics. Ser Qual Reliab Eng Stat Math Stat Methods Reliab 7:159–175\nBasak I, Balakrishnan N (2009) Predictors of failure times of censored items in progressively censored samples from normal distribution. Sankhya Indian J Stat 71–B(part 2):222–249\nBasak I, Basak P, Balakrishnan N (2006) On some predictors of times to failure of censored items in progressively censored samples. Comput Stat Data Anal 50:1313–1337\nDeGroot MH, Goel PK (1979) Bayesian estimation and optimal design in partially accelerated life testing. Nav Res Logist Q 26:223–235\nGannoun A, Saracco J, Yu K (2003) Nonparametric prediction by conditional median and quantiles. J Stat Plan Inference 117:207–223\nGouno E, Balakrishnan N (2001) Step-stress accelerated life test. In: Balakrishnan N, Rao CR (eds) Handbook of statistics—advances in reliability, vol 20. North-Holland, Amsterdam, pp 623–639\nGouno E, Sen A, Balakrishnan N (2004) Optimal step-stress test under progressive Type-I censoring. IEEE Trans Reliab 53:383–393\nHan N, Balakrishnan N, Sen A, Gouno E (2006) Corrections on optimal step-stress test under progressive Type-I censoring. IEEE Trans Reliab 55:613–614\nKaminsky KS, Nelson PI (1998) Prediction of order statistics. In: Balakrishnan N, Rao CR (eds) Handbook of statistics, 17—order statistics: applications. North-Holland, Amsterdam, pp 431–450\nKaminsky KS, Rhodin LS (1985) Maximum likelihood prediction. Ann Inst Stat Math 37:707–717\nKateri M, Balakrishnan N (2008) Inference for a simple step-stress model with Type-II censoring and Weibull distributed lifetimes. IEEE Trans Reliab 57:616–626\nKhamis IH, Higgins JJ (1998) A new model for step-stress testing. IEEE Trans Reliab 47:131–134\nLee J, Elmore R, Kennedy C, Gray M, Jones W (2011) Lifetime prediction for degradation of solar mirrors under step-stress accelerated testing. In: 2011 workshop on accelerated stress testing and reliability\nMiller R, Nelson WB (1983) Optimum simple step-stress plans for accelerated life testing. IEEE Trans Reliab 32:59–65\nNelson W (1982) Applied life data analysis. Wiley, New York\nRaqab MZ (1997) Modified maximum likelihood predictors of future order statistics from normal samples. Comput Stat Data Anal 25:91–106\nRaqab MZ (2004) Approximate maximum likelihood predictors of future failure times of shifted exponential distribution under multiple type-II censoring. Stat Methods Appl 13:43–54\nRaqab MZ, Nagaraja HN (1995) On some predictors of future order statistics. IMetron LIII–N.1–2:185–204\nRaqab MZ, Ahmadi J, Arabli BA (2013) Comparisons among some predictors of exponential distributions using pitman closeness. Comput Stat 28:2349–2356\nTang LC (2003) Multiple steps step-stress accelerated tests. In: Pham H (ed) Handbook of reliability engineering. Springer, New York, pp 441–455\nXiong C (1998) Inference on a simple step-stress model with Type-II censored exponential data. IEEE Trans Reliab 47:142–146\nXiong C, Ji M (2004) Analysis of grouped and censored data from step-stress life test. IEEE Trans Reliab 52:22–28\nXiong C, Milliken GA (2002) Prediction for exponential lifetimes based on step-stress testing. Commun Stat Simul Comput 31:539–556\nYang C-H, Tong L-I (2006) Predicting Type-II censored data from factorial experiments using modified maximum likelihood predictor. Int J Adv Manuf Technol 30:887–896",{"VOID":786},"10.1007\u002Fs00180-016-0684-0","2024-05-02T05:12:28.631+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00180-016-0684-0",[790,805],{"id":791,"sortIndex":21,"researcher":20,"roles":792,"affiliations":793,"properties":802,"displayName":804,"givenName":20,"familyName":20},"5d329901-8f04-446a-8e1a-eae710a85355",[133],[794],{"id":795,"sortIndex":21,"affiliation":796,"properties":20},"2529b0d0-f966-428b-acf9-b7acd22eaca8",{"id":795,"createTime":20,"updateTime":20,"relativeEntities":797,"slug":20,"properties":798,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":801,"statistic":20},[],{"title":799},{"VI":800},"Penn State Altoona, Altoona, USA",[],{"title":803},{"VI":804},"Indrani Basak",{"id":806,"sortIndex":99,"researcher":20,"roles":807,"affiliations":808,"properties":817,"displayName":819,"givenName":20,"familyName":20},"b57b6748-cb6c-4fe4-9e16-7ea1a1cb93ec",[133],[809],{"id":810,"sortIndex":21,"affiliation":811,"properties":20},"18a3d963-c9b5-4923-85be-51575324ea72",{"id":810,"createTime":20,"updateTime":20,"relativeEntities":812,"slug":20,"properties":813,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":816,"statistic":20},[],{"title":814},{"VI":815},"McMaster University, Hamilton, Canada",[],{"title":818,"gsAuthor":820},{"VI":819},"N. Balakrishnan",{"VOID":821},"[\"y6LPyPUAAAAJ\"]",{"url":20,"publisher":823,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":824,"slug":10,"properties":825,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":829,"manageAffiliations":842,"indexDatabases":853,"url":94,"thumbnailPath":20,"statistic":868,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":826,"title":827,"eissn":828},{"VOID":13},{"EN":15},{"VOID":17},[830,834,838],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":831,"label":832,"description":833,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":835,"label":836,"description":837,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":839,"label":840,"description":841,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[843,848],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":844,"slug":20,"properties":845,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":847,"statistic":20},[],{"title":846},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":849,"slug":20,"properties":850,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":852,"statistic":20},[],{"title":851},{"EN":55},[49],[854,861],{"id":59,"indexDatabase":855,"url":72,"indexYears":20,"academicFieldIds":860,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":856,"label":857,"description":858,"key":68,"publicationTags":859,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":862,"url":87,"indexYears":88,"academicFieldIds":867,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":863,"label":864,"description":865,"key":84,"publicationTags":866,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":869,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":870,"totalCitation":21,"totalCitationByYear":871,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":872,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"total":21,"publishYear":500,"statisticByYear":874},{},"2016-09-13","2026-07-30T16:28:44.844+00:00",[93,70],{"id":879,"createTime":880,"updateTime":881,"relativeEntities":882,"slug":883,"properties":884,"entityType":125,"verifyStatus":253,"verifyTime":893,"verifyNote":255,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":894,"fullTextUrl":20,"authors":895,"publicationType":176,"publisherRelationship":956,"citationCount":21,"citationInfo":1012,"publishDate":1015,"publishYear":1013,"citationAnalyzeStatus":19,"lastCitationAnalyze":881,"indexDatabases":1016,"openAccess":20,"references":1017,"isForceReanalyzing":236},"01ab9eb4-d58a-4051-8069-cf183b1f67f2","2024-01-16T19:55:25.789+00:00","2026-07-27T20:35:41.045+00:00",[],"Optimal-subsample-selection-for-massive-logistic-regression-with-distributed-data",{"abstract":885,"title":887,"gsPaper":889,"doi":891},{"EN":886},"With the emergence of big data, it is increasingly common that the data are distributed. i.e., the data are stored at many distributed sites (machines or nodes) owing to data collection or business operations, etc. We propose a distributed subsampling procedure in such a setting to efficiently approximate the maximum likelihood estimator for the logistic regression. We establish the consistency and asymptotic normality of the subsample estimator given the full data. The optimal subsampling probabilities and optimal allocation sizes are explicitly obtained. We develop a two-step algorithm to approximate the optimal subsampling procedure. Numerical simulations and an application to airline data are presented to evaluate the performance of our subsampling method.",{"EN":888},"Optimal subsample selection for massive logistic regression with distributed data",{"VOID":890},"[\"1716732002683402945\"]",{"VOID":892},"10.1007\u002Fs00180-021-01089-0","2024-05-04T15:50:07.770+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00180-021-01089-0",[896,911,924,941],{"id":897,"sortIndex":21,"researcher":20,"roles":898,"affiliations":899,"properties":908,"displayName":910,"givenName":20,"familyName":20},"eaf1c18f-4bda-4d22-b54f-3018bce9714f",[133],[900],{"id":901,"sortIndex":21,"affiliation":902,"properties":20},"b2fbd946-48b9-4df9-99ae-41fb725598a7",{"id":901,"createTime":20,"updateTime":20,"relativeEntities":903,"slug":20,"properties":904,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":907,"statistic":20},[],{"title":905},{"VI":906},"Center for Applied Mathematics, Tianjin University, Tianjin, China",[],{"title":909},{"VI":910},"Lulu Zuo",{"id":912,"sortIndex":99,"researcher":20,"roles":913,"affiliations":914,"properties":921,"displayName":923,"givenName":20,"familyName":20},"30d8dee5-63d5-4e2a-8570-0cdeb322a4c9",[133],[915],{"id":901,"sortIndex":21,"affiliation":916,"properties":20},{"id":901,"createTime":20,"updateTime":20,"relativeEntities":917,"slug":20,"properties":918,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":920,"statistic":20},[],{"title":919},{"VI":906},[],{"title":922},{"VI":923},"Haixiang Zhang",{"id":925,"sortIndex":100,"researcher":20,"roles":926,"affiliations":927,"properties":936,"displayName":938,"givenName":20,"familyName":20},"a6cf447f-801c-427f-ac93-a357853f43aa",[133],[928],{"id":929,"sortIndex":21,"affiliation":930,"properties":20},"444d239c-b2c3-4d78-8070-dde1689f0dd5",{"id":929,"createTime":20,"updateTime":20,"relativeEntities":931,"slug":20,"properties":932,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":935,"statistic":20},[],{"title":933},{"VI":934},"Department of Statistics, University of Connecticut, Storrs, Mansfield, USA",[],{"title":937,"gsAuthor":939},{"VI":938},"HaiYing Wang",{"VOID":940},"[\"SHd2S_0AAAAJ\"]",{"id":942,"sortIndex":101,"researcher":20,"roles":943,"affiliations":944,"properties":953,"displayName":955,"givenName":20,"familyName":20},"5db1aee9-e1fa-4ae3-8b46-52caf68f46eb",[133],[945],{"id":946,"sortIndex":21,"affiliation":947,"properties":20},"0ad8f483-0b1f-465f-9231-61757f9ca2ea",{"id":946,"createTime":20,"updateTime":20,"relativeEntities":948,"slug":20,"properties":949,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":952,"statistic":20},[],{"title":950},{"VI":951},"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China",[],{"title":954},{"VI":955},"Liuquan Sun",{"url":894,"publisher":957,"properties":1007},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":958,"slug":10,"properties":959,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":963,"manageAffiliations":976,"indexDatabases":987,"url":94,"thumbnailPath":20,"statistic":1002,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":960,"title":961,"eissn":962},{"VOID":13},{"EN":15},{"VOID":17},[964,968,972],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":965,"label":966,"description":967,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":969,"label":970,"description":971,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":973,"label":974,"description":975,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[977,982],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":978,"slug":20,"properties":979,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":981,"statistic":20},[],{"title":980},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":983,"slug":20,"properties":984,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":986,"statistic":20},[],{"title":985},{"EN":55},[49],[988,995],{"id":59,"indexDatabase":989,"url":72,"indexYears":20,"academicFieldIds":994,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":990,"label":991,"description":992,"key":68,"publicationTags":993,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":996,"url":87,"indexYears":88,"academicFieldIds":1001,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":997,"label":998,"description":999,"key":84,"publicationTags":1000,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":1003,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":1004,"totalCitation":21,"totalCitationByYear":1005,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1006,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"pages":1008,"volume":1010},{"VOID":1009},"2535-2562",{"VOID":1011},"36",{"total":21,"publishYear":1013,"statisticByYear":1014},2021,{},"2021-02-27",[93,70],[1018,1025,1031,1034,1037,1040,1043,1046,1049,1052,1055,1058,1061,1064,1067,1074,1077],{"id":20,"text":1019,"url":1020,"identifiers":1021},"Ai M, Yu J, Zhang H, Wang H (2020) Optimal subsampling algorithms for big data generalized linear models. Stat Sin. https:\u002F\u002Fdoi.org\u002F10.5705\u002Fss.202018.0439","https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.06761",{"mag":1022,"openalex":1023,"doi":1024},"2808239491","W2808239491","10.5705\u002Fss.202018.0439",{"id":1026,"text":1027,"url":1028,"identifiers":1029},"4c68646b-0035-4279-8000-0006b275d4fa","Battey H, Fan J, Liu H, Lu J, Zhu Z (2018) Distributed testing and estimation under sparse high dimensional models. Ann Stat 46:1352–1382","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":1030},"10.1007\u002Fs10440-022-00541-7",{"id":20,"text":1032,"url":20,"identifiers":1033},"Corbett J, Dean J, Epstein M et al (2013) Spanner: Google’s globally distributed database. ACM Trans Comput Syst 31, Article No. 8",{},{"id":1026,"text":1035,"url":1028,"identifiers":1036},"Ferguson T (1996) A course in large sample theory. Chapman and Hall, New York",{"doi":1030},{"id":1026,"text":1038,"url":1028,"identifiers":1039},"Jordan M, Lee J, Yang Y (2019) Communication-efficient distributed statistical inference. J Am Stat Assoc 114:668–681",{"doi":1030},{"id":1026,"text":1041,"url":1028,"identifiers":1042},"Kiefer J (1959) Optimum experimental designs. J R Stat Soc B 21:272–319",{"doi":1030},{"id":1026,"text":1044,"url":1028,"identifiers":1045},"Ma P, Mahoney M, Yu B (2015) A statistical perspective on algorithmic leveraging. J Mach Learn Res 16:861–911",{"doi":1030},{"id":1026,"text":1047,"url":1028,"identifiers":1048},"Schifano E, Wu J, Wang C, Yan J, Chen M (2016) Online updating of statistical inference in the big data setting. Technometrics 58:393–403",{"doi":1030},{"id":1026,"text":1050,"url":1028,"identifiers":1051},"Shi C, Lu W, Song R (2018) A massive data framework for M-estimators with cubic-rate. J Am Stat Assoc 113:1698–1709",{"doi":1030},{"id":1026,"text":1053,"url":1028,"identifiers":1054},"van der Vaart A (1998) Asymptotic statistics. Cambridge University Press, London",{"doi":1030},{"id":1026,"text":1056,"url":1028,"identifiers":1057},"Volgushev S, Chao S, Cheng G (2019) Distributed inference for quantile regression processes. Ann Stat 47:1634–1662",{"doi":1030},{"id":1026,"text":1059,"url":1028,"identifiers":1060},"Wang H (2019) More efficient estimation for logistic regression with optimal subsample. J Mach Learn Res 20:1–59",{"doi":1030},{"id":1026,"text":1062,"url":1028,"identifiers":1063},"Wang H, Zhu R, Ma P (2018) Optimal subsampling for large sample Logistic regression. J Am Stat Assoc 113:829–844",{"doi":1030},{"id":1026,"text":1065,"url":1028,"identifiers":1066},"Wang H, Yang M, Stufken J (2019) Information-based optimal subdata selection for big data linear regression. J Am Stat Assoc 114:393–405",{"doi":1030},{"id":20,"text":1068,"url":1069,"identifiers":1070},"Zhang T, Ning Y, Ruppert D (2020) Optimal sampling for generalized linear models under measurement constraints. J Comput Graph Stat. https:\u002F\u002Fdoi.org\u002F10.1080\u002F10618600.2020.1778483","https:\u002F\u002Fdoi.org\u002F10.1080\u002F10618600.2020.1778483",{"mag":1071,"openalex":1072,"doi":1073},"3034724369","W3034724369","10.1080\u002F10618600.2020.1778483",{"id":1026,"text":1075,"url":1028,"identifiers":1076},"Zhao T, Cheng G, Liu H (2016) A partially linear framework for massive heterogeneous data. Ann Stat 44:1400–1437",{"doi":1030},{"id":1026,"text":1078,"url":1028,"identifiers":1079},"Zuo L, Zhang H, Wang H, Liu L (2021) Sampling-based estimation for massive survival data with additive hazards model. Stat Med 40:441–450",{"doi":1030},{"id":1081,"createTime":1082,"updateTime":1083,"relativeEntities":1084,"slug":1085,"properties":1086,"entityType":125,"verifyStatus":253,"verifyTime":1097,"verifyNote":255,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1098,"fullTextUrl":20,"authors":1099,"publicationType":176,"publisherRelationship":1115,"citationCount":21,"citationInfo":1171,"publishDate":1174,"publishYear":1172,"citationAnalyzeStatus":19,"lastCitationAnalyze":1175,"indexDatabases":1176,"openAccess":20,"references":20,"isForceReanalyzing":236},"5633a775-f655-47a1-80d6-523b1a097004","2023-12-08T03:48:19.583+00:00","2026-07-23T03:48:36.786+00:00",[],"Asymptotic-cumulants-of-the-parameter-estimators-in-item-response-theory",{"abstract":1087,"title":1089,"gsPaper":1091,"references":1093,"doi":1095},{"EN":1088},"The asymptotic cumulants of the parameter estimators for the three-parameter logistic model in item response theory are derived up to the fourth order with the higher-order added asymptotic variances. The asymptotic cumulants of the corresponding Studentized estimators up to the third order are also given. The estimators are obtained by marginal maximum likelihood using the standard normal distribution for the latent variable with and without model misspecification. Numerical examples with fixed guessing parameters show advantages of the asymptotic expansions over the usual normal approximation.",{"EN":1090},"Asymptotic cumulants of the parameter estimators in item response theory",{"VOID":1092},"[\"13497820144904317743\"]",{"VOID":1094},"Bock RD (1972) Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika 37: 29–51\nBock RD, Aitkin M (1981) Marginal maximum likelihood estimation of item parameters: application of an EM algorithm. Psychometrika 46: 443–459\nBock RD, Lieberman M (1970) Fitting a response model for n dichotomously scored items. Psychometrika 35: 179–197\nBock RD, Moustaki I (2007) Item response theory in a general framework. In: Rao CR, Sinharay S(eds) Handbook of Statistics, vol 26. Psychometrics. Elsevier, New York, pp 469–513\nHall P (1992a) The bootstrap and Edgeworth expansion. Springer, New York Corrected printing, 1997\nHall P (1992b) On the removal of skewness by transformation. J Roy Stat Soc B 54: 221–228\nLord FM (1980) Applications of item response theory to practical testing problems. Erlbaum, Hillsdale\nLord FM, Novick MR (1968) Statistical theories of mental test scores. Addison-Wesley, Reading\nMislevy RJ (1984) Estimating latent distributions. Psychometrika 49: 359–381\nOgasawara H (2002) Stable response functions with unstable item parameter estimates. Appl Psychol Meas 26: 239–254\nOgasawara H (2006) Asymptotic expansion of the sample correlation coefficient under nonnormality. Comput Stat Data Anal 50: 891–910\nOgasawara H (2007) Higher-order estimation error in structural equation modeling. Econ Rev, Otaru University of Commerce. 57(4), 131–160 http:\u002F\u002Fwww.res.otaru-uc.ac.jp\u002F~hogasa\u002F\nStuart A, Ord JK (1994) Kendall’s advanced theory of statistics: distribution theory (6th edn, vol 1). Arnold, London\nThissen D, Wainer H (1982) Some standard errors in item response theory. Psychometrika 47: 397–412\nWirth RJ, Edwards MC (2007) Item factor analysis: current approaches and future directions. Psychol Methods 12: 58–79",{"VOID":1096},"10.1007\u002Fs00180-008-0118-8","2024-05-12T04:07:11.149+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs00180-008-0118-8",[1100],{"id":1101,"sortIndex":21,"researcher":20,"roles":1102,"affiliations":1103,"properties":1112,"displayName":1114,"givenName":20,"familyName":20},"218897c8-3d1e-43cb-8fdd-3b744afb102b",[133],[1104],{"id":1105,"sortIndex":21,"affiliation":1106,"properties":20},"6b55eb95-2643-4ed2-8019-45983fe80535",{"id":1105,"createTime":20,"updateTime":20,"relativeEntities":1107,"slug":20,"properties":1108,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1111,"statistic":20},[],{"title":1109},{"VI":1110},"Department of Information and Management Science, Otaru University of Commerce, Otaru, Japan",[],{"title":1113},{"VI":1114},"Haruhiko Ogasawara",{"url":1098,"publisher":1116,"properties":1166},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1117,"slug":10,"properties":1118,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1122,"manageAffiliations":1135,"indexDatabases":1146,"url":94,"thumbnailPath":20,"statistic":1161,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":1119,"title":1120,"eissn":1121},{"VOID":13},{"EN":15},{"VOID":17},[1123,1127,1131],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1124,"label":1125,"description":1126,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1128,"label":1129,"description":1130,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":1132,"label":1133,"description":1134,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[1136,1141],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":1137,"slug":20,"properties":1138,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1140,"statistic":20},[],{"title":1139},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":1142,"slug":20,"properties":1143,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1145,"statistic":20},[],{"title":1144},{"EN":55},[49],[1147,1154],{"id":59,"indexDatabase":1148,"url":72,"indexYears":20,"academicFieldIds":1153,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":1149,"label":1150,"description":1151,"key":68,"publicationTags":1152,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":1155,"url":87,"indexYears":88,"academicFieldIds":1160,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":1156,"label":1157,"description":1158,"key":84,"publicationTags":1159,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":1162,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":1163,"totalCitation":21,"totalCitationByYear":1164,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1165,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"pages":1167,"volume":1169},{"VOID":1168},"313-331",{"VOID":1170},"24",{"total":21,"publishYear":1172,"statisticByYear":1173},2008,{},"2008-05-15","2026-07-23T03:48:36.784+00:00",[93,70],{"id":1178,"createTime":1179,"updateTime":1180,"relativeEntities":1181,"slug":1182,"properties":1183,"entityType":125,"verifyStatus":253,"verifyTime":1193,"verifyNote":255,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1194,"fullTextUrl":20,"authors":1195,"publicationType":176,"publisherRelationship":1241,"citationCount":20,"citationInfo":20,"publishDate":1296,"publishYear":1013,"citationAnalyzeStatus":501,"lastCitationAnalyze":1297,"indexDatabases":1298,"openAccess":20,"references":20,"isForceReanalyzing":236},"82b22687-d525-43bf-8162-7d88a64a85b3","2023-12-13T04:26:52.917+00:00","2026-07-20T15:09:35.279+00:00",[],"Bayesian-Multiple-Change-Points-Detection-in-a-Normal-Model-with-Heterogeneous-Variances",{"abstract":1184,"title":1186,"gsPaper":1188,"references":1189,"doi":1191},{"EN":1185},"This study considers the problem of multiple change-points detection. For this problem, we develop an objective Bayesian multiple change-points detection procedure in a normal model with heterogeneous variances. Our Bayesian procedure is based on a combination of binary segmentation and the idea of the screening and ranking algorithm (Niu and Zhang in Ann Appl Stat 6:1306–1326, 2012). Using the screening and ranking algorithm, we can overcome the drawbacks of binary segmentation, as it cannot detect a small segment of structural change in the middle of a large segment or segments of structural changes with small jump magnitude. We propose a detection procedure based on a Bayesian model selection procedure to address this problem in which no subjective input is considered. We construct intrinsic priors for which the Bayes factors and model selection probabilities are well defined. We find that for large sample sizes, our method based on Bayes factors with intrinsic priors is consistent. Moreover, we compare the behavior of the proposed multiple change-points detection procedure with existing methods through a simulation study and two real data examples.",{"EN":1187},"Bayesian Multiple Change-Points Detection in a Normal Model with Heterogeneous Variances",{"VOID":367},{"VOID":1190},"Arlot S, Celisse A (2011) Segmentation of the mean of heteroscedastic data via cross-validation. Stat Comput 21:613–632\nBai J, Perron P (2003) Computation and analysis of multiple structural change models. J Appl Econometrics 18:1–22\nBarry D, Hartigan J (1993) A Bayesian analysis for change-point problems. J Am Stat Assoc 88:309–319\nBerger JO, Pericchi LR (1996) The intrinsic Bayes factor for model selection and prediction. J Am Stat Assoc 91:109–122\nBerger JO, Pericchi LR (1997) On justification of default and intrinsic Bayes factor. In: Lee JC et al (eds) Modeling and prediction. Springer-Verlag, New York, pp 276–293\nBerger JO, Pericchi LR (1998) On criticism and comparison of default Bayes factor for model selection and hypoghesis testing. In: Racugno W (ed) Proceedings of the Workshop on Model Selection. Pitagora, Bologna, pp 1–50\nBraun JV, Müller HG (1998) Statistical methods for DNA sequence segmentation. Stat Sci 13:142–162\nDe Santis F, Spezzaferri F (1999) Methods for default and robust Bayesian model comparison: the fractional Bayes factor approach. Int Stat Rev 67:267–286\nFearnhead P (2006) Exact and efficient Bayesian inference for multiple changepoint problems. Stat Comput 16:203–213\nFearnhead P, Clifford P (2003) Online inference for well-log data. J R Stat Soc Ser B 65:887–899\nFearnhead P, Liu Z (2007) On-line inference for multiple changepoint problems. J R Stat Soc Ser B 69:589–605\nFrick K, Munk A, Sieling H (2014) Multiscale change-point inference. J R Stat Soc B 76:495–580\nFryziewicz P (2014) Wild binary segmentation for multiple change-point detection. Ann Stat 42:2243–2281\nGiordani P, Kohn R (2008) Efficient Bayesian inference for multiple change-point and mixture innovation models. J Bus Econ Stat 26:66–77\nHao N, Niu YS, Zhang H (2013) Multiple change-point detection via a screening and ranking algorithm. Stat Sin 23:1553–1572\nHaynes K, Eckley IA, Fearnhead P (2014). Efficient penalty search for multiple changepoint problems. arXiv:1412.3617\nJeffreys H (1961) Theory of probability, 3rd edn. Oxford University Press, Oxford, UK\nJensen G (2013) Closed-form estimation of multiple change-point models. PeerJ PrePrint, 1:e90v3 https:\u002F\u002Fdoi.org\u002F10.7287\u002Fpeerj.preprints.90v3\nKass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90:773–795\nKillick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost. J Am Stat Assoc 107:1590–1598\nKillick R, Eckley IA, Jonathan P (2013) A wavelet-based approach for detecting changes in second order structure within nonstationary time series. Electron J Stat 7:1167–1183\nMoreno E (1997) Bayes factor for intrinsic and fractional priors in nested models: Bayesian Robustness. In: Yadolah D (ed) L1-statistical procddures and related topics, vol 31. Institute of Mathematical Statistics, Hayward, pp 257–270\nMoreno E, Bertolino F, Racugno W (1998) An intrinsic limiting procedure for model selection and hypotheses testing. J Am Stat Assoc 93:1451–1460\nMoreno E, Bertolino F, Racugno W (1999) Default Bayesian analysis of the Behrens-Fisher problem. J Stat Plan Inference 81:323–333\nMuggeo VMR, Adelfio G (2011) Efficient change point detection for genomic sequences of continuous measurements. Bioinformatics 27:161–166\nNiu YS, Zhang H (2012) The screening and ranking algorithm to detect DNA copy number variations. Ann Appl Stat 6:1306–1326\nNiu YS, Hao N, Zhang H (2016) Multiple change-point detection: a selective overview. Stat Sci 31:611–623\nO’Hagan A (1995) Fractional bayes factors for model comparison (with discussion). J R Stat Soc B 57:99–138\nO’Hagan A (1997) Properties of intrinsic and fractional Bayes factors. Test 6:101–118\nOlshen AB, Venkatraman ES, Lucito R, Wigler M (2004) Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5:557–572\nPein F, Sieling H, Munk A (2017) Heterogeneous change point inference. J R Stat Soc B 79:1207–1227\nReeves J, Chen J, Wang XL, Lund R, Lu Q (2007) A review and comparison of changepoint detection techniques for climate data. J Appl Meteorol Climatol 46:900–915\nRuanaidh JJK, Fitzgerald WJ (1996) Numerical bayesion methods applied to signal processing. Springer, New York\nSchwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464\nScott AJ, Knott M (1974) A Cluster analysis method for grouping means in the analysis of variance. Biometrics 30:507–512\nTibshirani R, Wang P (2008) Spatial smoothing and hot spot detection for CGH data using the fused lasso. Biostatistics 9:18–29\nVostrikova LJ (1981) Detecting disorder in multidimensional random processes. Soviet Mathematics: Doklady 24:55–59\nWhiteley N, Andrieu C, Doucet A (2011) Bayesian computational methods for inference in multiple change-points models. University of Bristol, Discussion Paper\nYao YC (1988) Estimating the number of change-points via Schwarz’ criterion. Stat Probab Lett 6:181–189\nZhang NR, Siegmund DO (2007) A modified bayes information criterion with applications to the analysis of comparative genomic hybridization data. Biometrics 63:22–32\nZhang NR, Siegmund DO (2012) Model selection for high-dimensional, multi-sequence change-point problems. Stat Sin 22:1057–1538",{"VOID":1192},"10.1007\u002Fs00180-020-01054-3","2024-06-26T17:20:37.714+00:00","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs00180-020-01054-3",[1196,1211,1226],{"id":1197,"sortIndex":21,"researcher":20,"roles":1198,"affiliations":1199,"properties":1208,"displayName":1210,"givenName":20,"familyName":20},"2039d23b-6c07-433a-ac12-b994835ff817",[133],[1200],{"id":1201,"sortIndex":21,"affiliation":1202,"properties":20},"6436b714-f302-429c-b559-206e31e5876e",{"id":1201,"createTime":20,"updateTime":20,"relativeEntities":1203,"slug":20,"properties":1204,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1207,"statistic":20},[],{"title":1205},{"VI":1206},"Department of Computer and Data Information, Sangji University, Wonju, Korea",[],{"title":1209},{"VI":1210},"Sang Gil Kang",{"id":1212,"sortIndex":99,"researcher":20,"roles":1213,"affiliations":1214,"properties":1223,"displayName":1225,"givenName":20,"familyName":20},"39bdcc65-99b2-40a0-b1d6-e74005935d65",[133],[1215],{"id":1216,"sortIndex":21,"affiliation":1217,"properties":20},"c679efc7-d447-46fc-b344-f4ec825c9920",{"id":1216,"createTime":20,"updateTime":20,"relativeEntities":1218,"slug":20,"properties":1219,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1222,"statistic":20},[],{"title":1220},{"VI":1221},"Pre-major of Cosmetics and Pharmaceutics, Daegu Haany University, Gyeongsan, Korea",[],{"title":1224},{"VI":1225},"Woo Dong Lee",{"id":1227,"sortIndex":100,"researcher":20,"roles":1228,"affiliations":1229,"properties":1238,"displayName":1240,"givenName":20,"familyName":20},"e58e3323-89a0-4f51-a62e-28a430afb051",[133],[1230],{"id":1231,"sortIndex":21,"affiliation":1232,"properties":20},"5203cc19-9e90-498a-8793-243dafece08b",{"id":1231,"createTime":20,"updateTime":20,"relativeEntities":1233,"slug":20,"properties":1234,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1237,"statistic":20},[],{"title":1235},{"EN":1236},"Department of Statistics, Kyungpook National University, Daegu, Korea.",[],{"title":1239},{"VI":1240},"Yongku Kim",{"url":1194,"publisher":1242,"properties":1292},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1243,"slug":10,"properties":1244,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1248,"manageAffiliations":1261,"indexDatabases":1272,"url":94,"thumbnailPath":20,"statistic":1287,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":1245,"title":1246,"eissn":1247},{"VOID":13},{"EN":15},{"VOID":17},[1249,1253,1257],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1250,"label":1251,"description":1252,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1254,"label":1255,"description":1256,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":1258,"label":1259,"description":1260,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[1262,1267],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":1263,"slug":20,"properties":1264,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1266,"statistic":20},[],{"title":1265},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":1268,"slug":20,"properties":1269,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1271,"statistic":20},[],{"title":1270},{"EN":55},[49],[1273,1280],{"id":59,"indexDatabase":1274,"url":72,"indexYears":20,"academicFieldIds":1279,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":1275,"label":1276,"description":1277,"key":68,"publicationTags":1278,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":1281,"url":87,"indexYears":88,"academicFieldIds":1286,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":1282,"label":1283,"description":1284,"key":84,"publicationTags":1285,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":1288,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":1289,"totalCitation":21,"totalCitationByYear":1290,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1291,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},{"pages":1293,"volume":1295},{"VOID":1294},"1365-1390",{"VOID":1011},"2021-01-12","2026-07-20T15:09:35.278+00:00",[93,70],{"id":1300,"createTime":1301,"updateTime":1302,"relativeEntities":1303,"slug":1304,"properties":1305,"entityType":125,"verifyStatus":253,"verifyTime":1316,"verifyNote":255,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1317,"fullTextUrl":20,"authors":1318,"publicationType":176,"publisherRelationship":1334,"citationCount":20,"citationInfo":20,"publishDate":1385,"publishYear":1386,"citationAnalyzeStatus":1387,"lastCitationAnalyze":1302,"indexDatabases":1388,"openAccess":20,"references":20,"isForceReanalyzing":236},"68ec1318-6248-4e65-80a2-b8cf6944b01c","2024-04-08T01:14:59.489+00:00","2026-07-19T04:13:50.572+00:00",[],"V-Berger-Selection-bias-and-covariate-imbalances-in-randomized-clinical-trials",{"abstract":1306,"title":1307,"gsPaper":1309,"keywords":1311,"references":1312,"doi":1314},{"EN":782},{"EN":1308},"V. Berger: Selection bias and covariate imbalances in randomized clinical trials",{"VOID":1310},"[\"5143000874404059756\"]",{"EN":782},{"VOID":1313},"Hu F and Rosenberger WF (2006). The theory of response-adaptive randomization in clinical trials. Wiley, New York\nPigeot I (2005). Book review. Biometrics 61(4): 1139–1140\nRosenberger WF and Lachin JM (2002). Randomization in clinical trials—theory and practice. Wiley, New York\nWang J (2006). Book review. Pharm Stat 5: 149–153",{"VOID":1315},"10.1007\u002Fs00180-007-0087-3","2024-05-07T13:18:35.056+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00180-007-0087-3",[1319],{"id":1320,"sortIndex":21,"researcher":20,"roles":1321,"affiliations":1322,"properties":1331,"displayName":1333,"givenName":20,"familyName":20},"41199bed-018c-45e8-81e9-bd1d5392ff78",[133],[1323],{"id":1324,"sortIndex":21,"affiliation":1325,"properties":20},"0be76a93-ff10-494e-bdc2-b3f28684b1d4",{"id":1324,"createTime":20,"updateTime":20,"relativeEntities":1326,"slug":20,"properties":1327,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1330,"statistic":20},[],{"title":1328},{"VI":1329},"Experimental Medicine Statistics, Merck Research Labs UG1D-44, North Wales, USA",[],{"title":1332},{"VI":1333},"Thomas E. Bradstreet",{"url":20,"publisher":1335,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1336,"slug":10,"properties":1337,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1341,"manageAffiliations":1354,"indexDatabases":1365,"url":94,"thumbnailPath":20,"statistic":1380,"gsStatistic":20,"type":104,"analyzePriority":20},[],{"issn":1338,"title":1339,"eissn":1340},{"VOID":13},{"EN":15},{"VOID":17},[1342,1346,1350],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":1343,"label":1344,"description":1345,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":1347,"label":1348,"description":1349,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},{"id":36,"createTime":20,"updateTime":20,"relativeEntities":1351,"label":1352,"description":1353,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":39},{},[1355,1360],{"id":43,"createTime":20,"updateTime":20,"relativeEntities":1356,"slug":20,"properties":1357,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1359,"statistic":20},[],{"title":1358},{"EN":47},[49],{"id":51,"createTime":20,"updateTime":20,"relativeEntities":1361,"slug":20,"properties":1362,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1364,"statistic":20},[],{"title":1363},{"EN":55},[49],[1366,1373],{"id":59,"indexDatabase":1367,"url":72,"indexYears":20,"academicFieldIds":1372,"indexDatabaseRanking":20},{"id":61,"createTime":20,"updateTime":20,"relativeEntities":1368,"label":1369,"description":1370,"key":68,"publicationTags":1371,"standard":20},[],{"EN":64,"VI":64},{"EN":66,"VI":67},[70,71],[74],{"id":76,"indexDatabase":1374,"url":87,"indexYears":88,"academicFieldIds":1379,"indexDatabaseRanking":93},{"id":78,"createTime":20,"updateTime":20,"relativeEntities":1375,"label":1376,"description":1377,"key":84,"publicationTags":1378,"standard":20},[],{"EN":81,"VI":81},{"EN":81,"VI":83},[86],[90,91,92],{"impactFactor":21,"impactFactorByYear":1381,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":97,"totalPublicationByYear":1382,"totalCitation":21,"totalCitationByYear":1383,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1384,"hindexLast5Year":21,"hindex":21},{},{"2000":99,"2004":99,"2005":100,"2006":100,"2008":99,"2010":100,"2011":100,"2012":99,"2015":99,"2018":99,"2019":101,"2020":99,"2022":100,"2023":99},{},{},"2007-09-13",2007,"DONE_ANALYZE_CITATION",[93,70]]