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Scand J Stat 12:171–178\nChoe SE, Boutros M, Michelson AM, Church GM, Halfon MS (2005) Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset. Genome Biol 6:R16\nEfron B, Tibshirani R, Storey JD, Tusher V (2001) Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96:1151–1160\nGao X (2006) Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments. Bioinformatics 22:1486–1494\nGottardo R, Raftery AE, KY Yeung, Bumgarner RE (2006) Bayesian robust inference for differential gene expression in microarrays with multiple samples. Biometrics 62:10–18\nIto K, Schull WJ (1964) On the robustness of the \\(T_{0}^{2}\\) test in multivariate analysis of variance when variance-covariance matrices are not equal. Biometrika 51:71–82\nLehmann EL, Romano JP (2005) Testing statistical hypotheses, 3rd edn. Springer texts in statistics. Springer, New York\nLockhart D, Dong B, Byrne M, Follettie M, Gallo M, Chee M, Mittman M (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 14:1675–1680\nMcLachlan G, Bean R, Jones LBT (2006) A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22:1608–1615\nPan W (2003) On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression. Bioinformatics 19:1333–1340\nPan W, Lin J, Le CT, (2003) A mixture model approach to detecting differentially expressed genes with microarray data. Funct Integr Genomics 3:117–124\nScheid S, Spang R (2006) In: Permutation filtering: a novel concept for significance analysis of large-scale genomic data. Lecture notes comput sci, vol 3909, pp 338–347\nScheid S, Spang R (2007) Compensating for unknown confounders in microarray data analysis using filtered permutations. J Comput Biol 14:669–681\nSouthworth LK, Kim SK, Owen AB (2009) Properties of balanced permutations. J Comput Biol 16:625–638\nStorey JD (2002) A direct approach to false discovery rates. J R Stat Soc B 64:479–498\nStorey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100:9440–9445\nTusher V, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98:5116–5121\nvan’t Wout AB, Lehrman GK, Mikheeva SA, O’Keeffe GC, Katze MG, Bumgarner RE, Geiss GK, Mullins JI (2003) Cellular gene expression upon human immunodeficiency virus type 1 infection of CD4+-T-cell lines. J Virol 77:1392–1402\nXie Y, Pan W, Khodursky AB (2005) A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data. Bioinformatics 21:4280–4288\nXu J, Cui X (2008) Robustified MANOVA with applications in detecting differentially expressed genes from oligonucleotide arrays. Bioinformatics 24:1056–1062\nZhao Y, Pan W (2003) Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments. Bioinformatics 19:1046–1054",{"EN":126},"Microarray data often consist of a large number of genes and a small number of replicates. We have examined testing the null hypothesis of equality of mean for detecting differentially expressed genes. The p-value for each gene is often estimated using permutation samples not only for the target gene but also for other genes. This method has been widely used and discussed. However, direct use of the permutation method for the p-value estimation may not work well, because two types of genes are mixed in the sample; some genes are differentially expressed, whereas others are not. To overcome this difficulty, various methods for appropriately generating null permutation samples have been proposed. In this paper, we consider two classes of test statistics that are naturally modified to null statistics. We then obtain the uniformly most powerful (UMP) unbiased tests among these classes. If the underlying distribution is symmetric, the UMP unbiased test statistic is similar to that proposed by Pan (Bioinformatics 19:1333–1340, 2003). Under another condition, the UMP unbiased test statistic has a different formula with one more degree of freedom and therefore is expected to give a more powerful test and a more accurate p-value estimation from a modified null statistic. In microarray data, because the number of replicates is often small, differences in the degree of freedom will produce large effects on the power of test and the variance of the p-value estimation. Some simulation studies and real data analyses are illustrated to investigate the performances of the methods.",{"EN":128},"Optimal significance analysis of microarray data in a class of tests whose null statistic can be constructed",{"VOID":130},"10.1007\u002Fs11749-011-0243-5","PUBLICATION","VERIFIED","Auto Verify","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11749-011-0243-5",[136,154],{"id":137,"sortIndex":108,"researcher":21,"roles":138,"affiliations":140,"properties":151},"316d9aa8-83b1-4c67-9648-0fce0efa38bc",[139],"AUTHOR",[141],{"id":21,"sortIndex":22,"affiliation":142,"properties":21},{"id":143,"createTime":144,"updateTime":145,"relativeEntities":146,"slug":147,"properties":148,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"a6bc3f60-87e8-4630-bb61-9ccc0beaea46","2023-12-26T03:46:54.249+00:00","2024-09-30T11:10:44.273+00:00",[],"Oita-University-of-Nursing-and-Health-sciences-Oita-Japan",{"title":149},{"VI":150},"Oita University of Nursing and Health sciences, Oita, Japan",{"title":152},{"VI":153},"Takayuki Sakaguchi",{"id":155,"sortIndex":22,"researcher":21,"roles":156,"affiliations":157,"properties":166},"1a50b6ab-28bd-432f-9d63-050b919cdd07",[139],[158],{"id":21,"sortIndex":22,"affiliation":159,"properties":21},{"id":160,"createTime":161,"updateTime":161,"relativeEntities":162,"slug":21,"properties":163,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"367d1bba-aad0-4200-8433-d56374202ffa","2023-12-31T20:02:16.887+00:00",[],{"title":164},{"VI":165},"The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan",{"title":167},{"VI":168},"Hironori Fujisawa","ARTICLE",{"url":134,"publisher":171,"properties":200},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":172,"slug":10,"properties":173,"entityType":19,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"subjectFields":178,"manageAffiliations":179,"indexDatabases":180,"url":21,"thumbnailPath":21,"statistic":195,"gsStatistic":21,"type":111,"analyzePriority":21},[],{"issn":174,"eissn":175,"title":176,"url":177},{"VOID":13},{"VOID":15},{"EN":10},{"VOID":18},[],[],[181,188],{"id":65,"indexDatabase":182,"url":78,"indexYears":79,"academicFieldIds":187,"indexDatabaseRanking":83},{"id":67,"createTime":68,"updateTime":69,"relativeEntities":183,"label":184,"description":185,"key":75,"publicationTags":186,"standard":21},[],{"EN":72,"VI":72},{"EN":72,"VI":74},[77],[81,82],{"id":85,"indexDatabase":189,"url":100,"indexYears":21,"academicFieldIds":194,"indexDatabaseRanking":21},{"id":87,"createTime":88,"updateTime":89,"relativeEntities":190,"label":191,"description":192,"key":96,"publicationTags":193,"standard":21},[],{"EN":92,"VI":92},{"VI":94,"EN":95},[98,99],[102],{"impactFactor":22,"impactFactorByYear":196,"i10Index":22,"i10IndexLast5Year":22,"totalPublication":105,"totalPublicationByYear":197,"totalCitation":22,"totalCitationByYear":198,"totalCitationPerPublication":22,"totalCitationPerPublicationByYear":199,"hindexLast5Year":22,"hindex":22},{},{"2019":107,"2020":108,"2021":108},{},{},{"volume":201,"pages":203},{"VOID":202},"21",{"VOID":204},"280-300","2011-04-30",2011,false,{"id":209,"createTime":210,"updateTime":211,"relativeEntities":212,"slug":213,"properties":214,"entityType":131,"verifyStatus":132,"verifyTime":211,"verifyNote":133,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"primaryUrl":221,"fullTextUrl":21,"authors":222,"publicationType":169,"publisherRelationship":238,"citationCount":21,"citationInfo":21,"publishDate":273,"publishYear":274,"citationAnalyzeStatus":20,"lastCitationAnalyze":21,"indexDatabases":21,"openAccess":21,"references":21,"isForceReanalyzing":207},"539052bd-43ef-4c27-ad62-f7cf47af2c8c","2024-01-14T22:19:31.436+00:00","2025-02-13T23:49:21.835+00:00",[],"Comments-on-Panel-data-analysis-advantages-and-challenges",{"references":215,"title":217,"doi":219},{"VOID":216},"Davidson R, Mackinnon JG (2004) Econometric theory and methods. Oxford University Press, New York\nde Finetti B (1930) Problemi determinati e indeterminati nel calculo delle probabilità. Rend R Accad Naz Lincei Ser 6 12(9)\nde Finetti B (1970) Teoria delle Probabilità: sintesi introduttiva con appendice critica. Giulio Einaudi Editorial, Torino. Translated as Theory of Probability, Chichester, 1990\nEngle RF, Hendry DF, Richard J-F (1983) Exogeneity. Econometrica 51:277–304\nHaavelmo T (1944) The probability approach in econometrics. Econometrica 12 (supplement)\nHeckman JJ (1991) Identifying the hand of the past: distinguishing state dependence from heterogeneity. Am Econ Rev 81(2):75–79\nLindley DV, Novick MR (1981) The role exchangeability in inference. Ann Stat 9:45–58\nNeyman J, Scott EL (1948) Consistent estimates based on partially consistent observations. Econometrica 16:1–32\nWooldridge JM (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge",{"EN":218},"Comments on: Panel data analysis—advantages and challenges",{"VOID":220},"10.1007\u002Fs11749-007-0052-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11749-007-0052-z",[223],{"id":224,"sortIndex":22,"researcher":21,"roles":225,"affiliations":226,"properties":235},"541a1965-92ab-485c-a78f-f211761842a9",[139],[227],{"id":21,"sortIndex":22,"affiliation":228,"properties":21},{"id":229,"createTime":230,"updateTime":230,"relativeEntities":231,"slug":21,"properties":232,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"2bebe900-00bc-4f9d-9197-09c717f09ee5","2024-01-14T22:19:31.560+00:00",[],{"title":233},{"VI":234},"Department of Agricultural and Resource Economics, University of Maryland, Maryland, USA",{"title":236},{"VI":237},"Marc Nerlove",{"url":221,"publisher":239,"properties":268},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":240,"slug":10,"properties":241,"entityType":19,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"subjectFields":246,"manageAffiliations":247,"indexDatabases":248,"url":21,"thumbnailPath":21,"statistic":263,"gsStatistic":21,"type":111,"analyzePriority":21},[],{"issn":242,"eissn":243,"title":244,"url":245},{"VOID":13},{"VOID":15},{"EN":10},{"VOID":18},[],[],[249,256],{"id":65,"indexDatabase":250,"url":78,"indexYears":79,"academicFieldIds":255,"indexDatabaseRanking":83},{"id":67,"createTime":68,"updateTime":69,"relativeEntities":251,"label":252,"description":253,"key":75,"publicationTags":254,"standard":21},[],{"EN":72,"VI":72},{"EN":72,"VI":74},[77],[81,82],{"id":85,"indexDatabase":257,"url":100,"indexYears":21,"academicFieldIds":262,"indexDatabaseRanking":21},{"id":87,"createTime":88,"updateTime":89,"relativeEntities":258,"label":259,"description":260,"key":96,"publicationTags":261,"standard":21},[],{"EN":92,"VI":92},{"VI":94,"EN":95},[98,99],[102],{"impactFactor":22,"impactFactorByYear":264,"i10Index":22,"i10IndexLast5Year":22,"totalPublication":105,"totalPublicationByYear":265,"totalCitation":22,"totalCitationByYear":266,"totalCitationPerPublication":22,"totalCitationPerPublicationByYear":267,"hindexLast5Year":22,"hindex":22},{},{"2019":107,"2020":108,"2021":108},{},{},{"volume":269,"pages":271},{"VOID":270},"16",{"VOID":272},"42-46","2007-03-13",2007,{"id":276,"createTime":277,"updateTime":278,"relativeEntities":279,"slug":280,"properties":281,"entityType":131,"verifyStatus":20,"verifyTime":278,"verifyNote":290,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"primaryUrl":291,"fullTextUrl":21,"authors":292,"publicationType":169,"publisherRelationship":454,"citationCount":21,"citationInfo":21,"publishDate":489,"publishYear":490,"citationAnalyzeStatus":20,"lastCitationAnalyze":21,"indexDatabases":21,"openAccess":21,"references":21,"isForceReanalyzing":207},"4cfdf770-49e0-4156-a22a-1512fb293c58","2023-11-28T07:20:31.266+00:00","2025-01-25T23:47:50.768+00:00",[],"Coherent-combination-of-experts-opinions",{"references":282,"abstract":284,"title":286,"doi":288},{"VOID":283},"Berger, J. O. and Mortera, J. (1991). Bayesian limited communication.J.Statist. Planning and Inference 28, 1–24.\nBordley, R. F. (1982). A multiplicative formula for aggregating probability assessments.Manag. Sci. 28, 1137–1148.\nCarnap, R. (1950).Logical Foundations of Probability. Chicago: University Press.\nClemen, R. T. (1985). Extraneous expert information.J. Forecasting 4, 329–348.\nClemen, R. T. (1986). Calibration and the aggregation of probabilities.Manag. Sci. 32, 312–314.\nClemen, R. T. and Murphy, A. H. (1986). Objective and subjective precipitation probability forecasts: statistical analysis of some interrelationships.Weather and Forecasting 1, 56–65.\nDawid, A. P. (1979). Conditional independence in statistical theory.J. Roy. Statist. Soc. B 41, 1–31, (with discussion).\nDawid, A. P. (1982). The well-calibrated Bayesian.J. Amer. Statist. Assoc. 77, 605–610.\nDawid, A. P. (1986). Probability forecasting.Encyclopedia of the Statistical Sciences (S. Kotz, N. L. Johnson and C. B. Read, eds.), vol.7. New York: Wiley, 210–218.\nDeGroot, M. H. (1974). Reaching a consensus.J. Amer. Statist. Assoc. 69, 118–121.\nDeGroot, M. H. (1988). A Bayesian view of assessing uncertainty and comparing expert opinion.J. Statist. Planning and Inference 20, 295–306.\nDeGroot, M. H. and Eriksson, E. A. (1985). Probability forecasting, stochastic dominance and the Lorenz curve.Bayesian Statistics 2, (J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith, eds.), Amsterdam: North-Holland, 99–118, (with discussion).\nDeGroot M. H. and Fienberg, S. E. (1983). The comparison and evaluation of forecasters.The Statistician 32, 12–22.\nDeGroot, M. H. and Mortera, J. (1991). Optimal linear opinion pools.Manag. Sci. 37, 546–558.\nFrench, S. (1980). Updating belief in the light of someone else's opinion.J. Roy. Statist. Soc. A 143, 43–48.\nFrench, S. (1981). Consensus of opinion.Europ. J. Oper. Res. 7, 332–340.\nFrench, S. (1985). Group consensus probability distribution: a critical survey.Bayesian Statistics 2 (J. M. Bernardo M. H. DeGroot, D. V. Lindley and A. F. M. Smith, eds.), Amsterdam: North-Holland, 183–201, (with discussion).\nFrench, S. (1986). Calibration and the expert problem.Manag. Sci. 32, 315–320.\nGenest, C. (1984). A characterization theorem for externally bayesian groups.Ann. Statist. 12, 1100–1105.\nGenest, C. and McConway K. J. (1990). Allocating the weights in the linear opinion pool.J. Forecasting 9, 53–73.\nGenest, C., McConway K. J. and Schervish, M. J. (1986). Characterization of externally Bayesian pooling operators.Ann. Statist. 14, 487–501.\nGenest, C. and Schervish, M. J. (1985). Modelling expert judgments for Bayesian updating.Ann. Statist. 13, 1198–1212.\nGenest, C. and Zidek, J. V. (1986). Combining probability distributions: a critique and an annotated bibliography.Statist. Sci. 1, 114–148.\nGutmann, S., Kemperman, J. H. B., Reeds J. A. and Shepp, L. A. (1991). Existence of probability measures with given marginals.Annals of Prob. 19, 1781–1797.\nJeffreys H. (1939).Theory of Probability. Oxford: Clarendon Press.\nKellerer, H. G. (1961). Funktionen und Produkträumen mit vorgegeben Marginal-Funktionen.Math. Ann. 144, 323–344.\nKeynes, J. M. (1921).A Treatise on Probability. London. Macmillan.\nLauritzen, S. L. (1980).Statistical Models as Extremal Families, Aalborg: University Press.\nLindley, D. V. (1982). The improvement of probability judgementsJ. Roy. Statist. Soc. B 145, 117–126.\nLindley, D. V. (1985). Reconciliation, of discrete probability distributions.Bayesian Statistics 2 (J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith, eds.), Amsterdam: North-Holland, 375–390, (with discussion).\nLindley, D. V. (1986). Another look at an axiomatic approach to expert resolution.Manag. Sci. 32, 303–306.\nMcConway, K. J. (1981). Marginalization and linear opinion pools.J. Amer. Statist. Assoc. 76, 410–414.\nMadansky, A. (1964). Externally Bayesian groups. RAND Memo RM-4141-PR. Santa Monica, CA: The Rand Corporation.\nMorris, P. A. (1983). An axiomatic approach to expert resolution.Manag. Sci. 29, 24–32.\nMurphy, A. H. and Winkler R. L. (1992). Diagnostic verification of probability forecasts.Int. J. Forecasting 7, 435–455.\nSchervish, M. J. (1986). Comments on some axioms for combining expert judgments.Manag. Sci. 32, 306–312.\nSingpurwalla, N. D. (1988). Discussion of “Gaining weight: A Bayesian Approach” by Bayarri, M. J. and DeGroot, M. H.Bayesian Statistics 2 (J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith, eds.), Amsterdam: North-Holland, 39–42.\nStone, M. (1961). The opinion pool.Ann. Math. Statist. 32, 1339–1342.\nStrassen, V. (1965). The existence of probability measures with given marginals.Ann. Math. Statist. 36, 423–439.\nWinkler, R. L. (1981). Combining probability distributions from dependent information sources.Manag. Sci. 27, 479–488.\nWinkler, R. L. (1986). Expert resolution.Manag. Sci. 32, 298–303.\nArrow, K. J. (1951).Social Choice and Individual Values. New York: Wiley.\nClemen, R. T. (1987). Combining overlapping information.Manag. Sci. 33, 373–380.\nClemen, R. T. and Winkler, R. L. (1990). Unanimity and compromise among probability forecasters.Manag. Sci. 36, 767–779.\nClemen, R. T. and Jouini, M. N. (1996). Copula models for aggregating expert opinions.Operations Research 44, (to appear).\nCooke, R. M. (1993). The ill advised BayesianISBA Newsletter 2, 2–3.\nDalkey N. C. (1972). An impossibility theorem for group probability functionsTech. Rep. P-4862. Santa Monica, CA: RAND Corporation.\nDraper, D. (1995). Assessment and propagation of model uncertainty.J. Roy. Statist. Soc. B 57, 45–97, (with discussion).\nFrench, S. (1994). Utility: probability's younger twin.Aspects of Uncertainty: a Tribute to D. V. Lindley (P. R. Freeman, and A. F. M. Smith, eds.), Chichester: Wiley, 171–180.\nHarper, F. T.; Goossens, L. H. J.; Cooke, R. M.; Helton, J. C.; Hora, S. C.; Jones, J. A.; Kraan, B. C. P.; Lui, C.; McKay, M. D.; Miller, L. A.; Päsler Sauer, J. and Young, M. L. (1994).Joint USNRC\u002FCEC Consequence Uncertainty Study: Summary of Objectives, Approach, Application and Results for the Dispersion and Deposition Uncertainty Assessments. NUREG\u002FCR-6244, EUR 15855, SAND94-1453. Sandia National Laboratories and Delft University of Technology.\nKelly, F. S. (1978).Arrow Impossibility Theorems. New York: Academic Press.\nMakridakis, S. and Winkler, R. L. (1983). Average of forecasts: some empirical results.Manag. Sci. 29, 987–996.",{"EN":285},"Anexpert (for You) is here defined as someone who shares Your world-view, but knows more than You do, so that were She to reveal Her current opinion to You, You would adopt it as Your own. When You have access to different experts, with differing information, You require acombination formula to aggregate their various opinions. A number of formulae have been suggested, but here we explore the fundamental requirement ofcoherence to relate such a formula to Your joint distribution for the experts' opinions. In particular, in the context of opinions about an uncertain eventA, we investigate coherence properties of the linear, harmonic and logarithmic opinion pools. Some general results on coherence of the joint forecast distribution are also developed.",{"EN":287},"Coherent combination of experts' opinions",{"VOID":289},"10.1007\u002FBF02562628","Author affiliation is blank","http:\u002F\u002Flink.springer.com\u002F10.1007\u002FBF02562628",[293,310,326,344,361,378,394,412,419,436],{"id":294,"sortIndex":60,"researcher":21,"roles":295,"affiliations":296,"properties":307},"c8108d66-9a67-41b6-85cc-c1a242579718",[139],[297],{"id":21,"sortIndex":22,"affiliation":298,"properties":21},{"id":299,"createTime":300,"updateTime":301,"relativeEntities":302,"slug":303,"properties":304,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"3e7eba27-43b5-47cf-8faf-4bd79d81b48e","2024-01-12T02:24:50.011+00:00","2025-01-30T01:48:10.794+00:00",[],"The-Open-University-UK",{"title":305},{"VI":306},"The Open University, UK",{"title":308},{"VI":309},"K. J. McConway",{"id":311,"sortIndex":107,"researcher":21,"roles":312,"affiliations":313,"properties":323},"5a166c79-9558-48bf-8b20-746752876ce1",[139],[314],{"id":21,"sortIndex":22,"affiliation":315,"properties":21},{"id":316,"createTime":317,"updateTime":317,"relativeEntities":318,"slug":319,"properties":320,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"26ca019c-971e-4805-8a31-9fb308a7f00b","2023-11-28T07:20:31.284+00:00",[],"Dipartimento-di-Economia-Universit%C3%A0-di-Roma-III-Rome-Italy",{"title":321},{"VI":322},"Dipartimento di Economia, Università di Roma III, Rome, Italy",{"title":324},{"VI":325},"J. Mortera",{"id":327,"sortIndex":328,"researcher":21,"roles":329,"affiliations":330,"properties":341},"de156420-5298-48d5-9beb-4334270029e9",6,[139],[331],{"id":21,"sortIndex":22,"affiliation":332,"properties":21},{"id":333,"createTime":334,"updateTime":335,"relativeEntities":336,"slug":337,"properties":338,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"b1e538d9-dce2-4700-8598-5e62fa2dadc2","2024-01-21T03:26:37.681+00:00","2024-10-03T02:43:19.155+00:00",[],"Carnegie-Mellon-University-USA",{"title":339},{"VI":340},"Carnegie-Mellon University USA",{"title":342},{"VI":343},"M. J. Schervish",{"id":345,"sortIndex":346,"researcher":21,"roles":347,"affiliations":348,"properties":358},"832eefa9-d41e-4d95-919b-15e7a42f1445",3,[139],[349],{"id":21,"sortIndex":22,"affiliation":350,"properties":21},{"id":351,"createTime":352,"updateTime":352,"relativeEntities":353,"slug":354,"properties":355,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"68cfb6f8-7531-4939-858b-769da1e74476","2023-11-28T07:20:31.292+00:00",[],"Technishe-Universiteit-Delft-The-Netherlands",{"title":356},{"VI":357},"Technishe Universiteit Delft, The Netherlands",{"title":359},{"VI":360},"R. Cooke",{"id":362,"sortIndex":105,"researcher":21,"roles":363,"affiliations":364,"properties":375},"6f67a908-287e-4363-9005-f6b61190e8dc",[139],[365],{"id":21,"sortIndex":22,"affiliation":366,"properties":21},{"id":367,"createTime":368,"updateTime":369,"relativeEntities":370,"slug":371,"properties":372,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"34fea5ac-f416-4f0a-925d-9122275cd7cb","2023-12-28T17:50:01.173+00:00","2025-07-10T16:10:05.086+00:00",[],"University-of-Leeds-UK",{"title":373},{"VI":374},"University of Leeds, UK",{"title":376},{"VI":377},"S. French",{"id":379,"sortIndex":380,"researcher":21,"roles":381,"affiliations":382,"properties":391},"dff578e0-d9b5-4cde-8aa6-99d79961aa66",7,[139],[383],{"id":21,"sortIndex":22,"affiliation":384,"properties":21},{"id":385,"createTime":386,"updateTime":386,"relativeEntities":387,"slug":21,"properties":388,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"699dc699-b2bd-4d9d-9e8e-757d01dd555c","2024-01-14T16:25:02.700+00:00",[],{"title":389},{"VI":390},"Somerset, UK",{"title":392},{"VI":393},"D. V. Lindley",{"id":395,"sortIndex":396,"researcher":21,"roles":397,"affiliations":398,"properties":409},"f3505607-d94c-4ed0-9b32-21f111e4e3c5",9,[139],[399],{"id":21,"sortIndex":22,"affiliation":400,"properties":21},{"id":401,"createTime":402,"updateTime":403,"relativeEntities":404,"slug":405,"properties":406,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"145c1c2c-a673-4645-905d-845bebbe16c4","2023-12-13T02:02:37.221+00:00","2025-01-27T16:28:11.546+00:00",[],"Duke-University-USA",{"title":407},{"VI":408},"Duke University USA",{"title":410},{"VI":411},"R. L. Winkler",{"id":413,"sortIndex":108,"researcher":21,"roles":414,"affiliations":415,"properties":416},"ec5dc53b-0621-4d17-b61d-c50b4618f204",[139],[],{"title":417},{"VI":418},"M. H. DeGroot",{"id":420,"sortIndex":22,"researcher":21,"roles":421,"affiliations":422,"properties":433},"74d1d9a0-c21d-4156-8eb5-53f0044670c6",[139],[423],{"id":21,"sortIndex":22,"affiliation":424,"properties":21},{"id":425,"createTime":426,"updateTime":427,"relativeEntities":428,"slug":429,"properties":430,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"9ab5525c-314a-4339-8585-112ed252a634","2024-01-15T07:32:11.378+00:00","2025-02-09T00:16:55.333+00:00",[],"Department-of-Statistical-Science-University-College-London-London-UK",{"title":431},{"VI":432},"Department of Statistical Science, University College London, London, UK",{"title":434},{"VI":435},"A. P. Dawid",{"id":437,"sortIndex":438,"researcher":21,"roles":439,"affiliations":440,"properties":451},"20df5537-4960-4d2f-992e-1971186e27e0",5,[139],[441],{"id":21,"sortIndex":22,"affiliation":442,"properties":21},{"id":443,"createTime":444,"updateTime":445,"relativeEntities":446,"slug":447,"properties":448,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"13f5a94e-67c4-428e-a517-5053526fecbe","2023-12-12T01:27:04.034+00:00","2024-10-02T21:32:03.201+00:00",[],"Universit%C3%A9-Laval-Canada",{"title":449},{"VI":450},"Université Laval, Canada",{"title":452},{"VI":453},"C. Genest",{"url":291,"publisher":455,"properties":484},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":456,"slug":10,"properties":457,"entityType":19,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"subjectFields":462,"manageAffiliations":463,"indexDatabases":464,"url":21,"thumbnailPath":21,"statistic":479,"gsStatistic":21,"type":111,"analyzePriority":21},[],{"issn":458,"eissn":459,"title":460,"url":461},{"VOID":13},{"VOID":15},{"EN":10},{"VOID":18},[],[],[465,472],{"id":65,"indexDatabase":466,"url":78,"indexYears":79,"academicFieldIds":471,"indexDatabaseRanking":83},{"id":67,"createTime":68,"updateTime":69,"relativeEntities":467,"label":468,"description":469,"key":75,"publicationTags":470,"standard":21},[],{"EN":72,"VI":72},{"EN":72,"VI":74},[77],[81,82],{"id":85,"indexDatabase":473,"url":100,"indexYears":21,"academicFieldIds":478,"indexDatabaseRanking":21},{"id":87,"createTime":88,"updateTime":89,"relativeEntities":474,"label":475,"description":476,"key":96,"publicationTags":477,"standard":21},[],{"EN":92,"VI":92},{"VI":94,"EN":95},[98,99],[102],{"impactFactor":22,"impactFactorByYear":480,"i10Index":22,"i10IndexLast5Year":22,"totalPublication":105,"totalPublicationByYear":481,"totalCitation":22,"totalCitationByYear":482,"totalCitationPerPublication":22,"totalCitationPerPublicationByYear":483,"hindexLast5Year":22,"hindex":22},{},{"2019":107,"2020":108,"2021":108},{},{},{"volume":485,"pages":487},{"VOID":486},"4",{"VOID":488},"263-313","1995-12-01",1995,{"id":492,"createTime":493,"updateTime":494,"relativeEntities":495,"slug":496,"properties":497,"entityType":131,"verifyStatus":132,"verifyTime":494,"verifyNote":133,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"primaryUrl":506,"fullTextUrl":21,"authors":507,"publicationType":169,"publisherRelationship":552,"citationCount":21,"citationInfo":21,"publishDate":585,"publishYear":586,"citationAnalyzeStatus":20,"lastCitationAnalyze":21,"indexDatabases":21,"openAccess":21,"references":21,"isForceReanalyzing":207},"eaa2c2aa-c392-45b6-97e4-b46d77153c92","2024-02-13T07:58:01.736+00:00","2025-01-08T23:46:32.978+00:00",[],"A-generalized-Hosmer-Lemeshow-goodness-of-fit-test-for-a-family-of-generalized-linear-models",{"references":498,"abstract":500,"title":502,"doi":504},{"VOID":499},"Agresti A (1996) An introduction to categorical data analysis. Wiley, New York\nBilder CR, Loughin TM (2014) Analysis of categorical data with R. Chapman and Hall\u002FCRC, Boston\nBlizzard L, Hosmer DW (2006) Parameter estimation and goodness-of-fit in log binomial regression. Biom J 48(1):5–22\nCanary JD (2013) Grouped goodness-of-fit tests for binary regression models. PhD thesis, University of Tasmania\nCanary JD, Blizzard L, Barry RP, Hosmer DW, Quinn SJ (2016) Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions. Biom J 58(3):674–690\nCheng KF, Wu JW (1994) Testing goodness of fit for a parametric family of link functions. J Am Stat Assoc 89(426):657–664\nChristensen R, Lin Y (2015) Lack-of-fit tests based on partial sums of residuals. Commun Stat Theory Methods 44(13):2862–2880\nFagerland MW, Hosmer DW (2013) A goodness-of-fit test for the proportional odds regression model. Stat Med 32(13):2235–2249\nFagerland MW, Hosmer DW (2016) Tests for goodness of fit in ordinal logistic regression models. J Stat Comput Simul 86(17):3398–3418\nFagerland MW, Hosmer DW, Bofin AM (2008) Multinomial goodness-of-fit tests for logistic regression models. Stat Med 27(21):4238–4253\nGonzález-Manteiga W, Crujeiras RM (2013) An updated review of goodness-of-fit tests for regression models. TEST 22(3):361–411\nHalteman WA (1980) A goodness of fit test for binary logistic regression. Unpublished doctoral dissertation, Department of Biostatistics, University of Washington, Seattle, WA\nHosmer DW, Hjort NL (2002) Goodness-of-fit processes for logistic regression: simulation results. Stat Med 21(18):2723–2738\nHosmer DW, Lemeshow S (1980) Goodness of fit tests for the multiple logistic regression model. Commun Stat Theory Methods 9(10):1043–1069\nLin DY, Wei LJ, Ying Z (2002) Model-checking techniques based on cumulative residuals. Biometrics 58(1):1–12\nLiu A, Meiring W, Wang Y (2004) Testing generalized linear models using smoothing spline methods. Stat Sin 15:235–256\nMoore DS, Spruill MC (1975) Unified large-sample theory of general chi-squared statistics for tests of fit. Ann Stat 3:599–616\nPulkstenis E, Robinson TJ (2002) Two goodness-of-fit tests for logistic regression models with continuous covariates. Stat Med 21(1):79–93\nQuinn SJ, Hosmer DW, Blizzard CL (2015) Goodness-of-fit statistics for log-link regression models. J Stat Comput Simul 85(12):2533–2545\nRodríguez-Campos MC, González-Manteiga W, Cao R (1998) Testing the hypothesis of a generalized linear regression model using nonparametric regression estimation. J Stat Plan Inference 67(1):99–122\nStute W, Zhu L-X (2002) Model checks for generalized linear models. Scand J Stat 29(3):535–545\nSu JQ, Wei LJ (1991) A lack-of-fit test for the mean function in a generalized linear model. J Am Stat Assoc 86(414):420–426\nSurjanovic N, Loughin TM (2021) Improving the Hosmer–Lemeshow goodness-of-fit test in large models with replicated trials. arXiv preprint arXiv:2102.12698\nTsiatis AA (1980) A note on a goodness-of-fit test for the logistic regression model. Biometrika 67(1):250–251\nWhite H (1982) Maximum likelihood estimation of misspecified models. Econometrica 50(1):1–25\nXiang D, Wahba G (1995) Testing the generalized linear model null hypothesis versus ‘smooth’ alternatives. Technical Report 953, Department of Statistics, University of Wisconsin",{"EN":501},"Generalized linear models (GLMs) are very widely used, but formal goodness-of-fit (GOF) tests for the overall fit of the model seem to be in wide use only for certain classes of GLMs. We develop and apply a new goodness-of-fit test, similar to the well-known and commonly used Hosmer–Lemeshow (HL) test, that can be used with a wide variety of GLMs. The test statistic is a variant of the HL statistic, but we rigorously derive an asymptotically correct sampling distribution using methods of Stute and Zhu (Scand J Stat 29(3):535–545, 2002) and demonstrate its consistency. We compare the performance of our new test with other GOF tests for GLMs, including a naive direct application of the HL test to the Poisson problem. Our test provides competitive or comparable power in various simulation settings and we identify a situation where a naive version of the test fails to hold its size. Our generalized HL test is straightforward to implement and interpret and an R package is publicly available.",{"EN":503},"A generalized Hosmer–Lemeshow goodness-of-fit test for a family of generalized linear models",{"VOID":505},"10.1007\u002Fs11749-023-00912-8","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11749-023-00912-8",[508,523,540],{"id":509,"sortIndex":22,"researcher":21,"roles":510,"affiliations":511,"properties":520},"94ffe632-cb91-4cac-96b5-7cc432333715",[139],[512],{"id":21,"sortIndex":22,"affiliation":513,"properties":21},{"id":514,"createTime":515,"updateTime":515,"relativeEntities":516,"slug":21,"properties":517,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"573ff5d2-373f-4261-b2fe-70560587afbf","2024-01-29T01:51:34.193+00:00",[],{"title":518},{"VI":519},"Department of Statistics, University of British Columbia, Vancouver, Canada",{"title":521},{"VI":522},"Nikola Surjanovic",{"id":524,"sortIndex":107,"researcher":21,"roles":525,"affiliations":526,"properties":537},"6886906f-a200-42bc-80e0-1802bf260d3f",[139],[527],{"id":21,"sortIndex":22,"affiliation":528,"properties":21},{"id":529,"createTime":530,"updateTime":531,"relativeEntities":532,"slug":533,"properties":534,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"a55ef75b-7172-4aeb-aa9e-7158f2faa258","2024-01-01T15:58:11.511+00:00","2025-01-28T11:47:51.529+00:00",[],"Department-of-Statistics-and-Actuarial-Science-Simon-Fraser-University-Burnaby-Canada",{"title":535},{"VI":536},"Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada",{"title":538},{"VI":539},"Thomas M. 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The paper proposes two general estimators—and a computationally attractive and asymptotically equivalent one-step version of them—that combine inverse probability weighting and robust local linear estimation. The paper also considers inference for the unknown infinite-dimensional parameter and proposes two Wald statistics that are shown to have power under a sequence of local Pitman drifts and are consistent as the drifts diverge. The results of the paper are illustrated with three examples: robust local generalized estimating equations, robust local quasi-likelihood and robust local nonlinear least squares estimation. 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J Multivar Anal 133:356–376\nAnderson TW (1951) Estimating linear restrictions on regression coefficients for multivariate normal distributions. Ann Math Stat 22(3):327–351\nBergesio A, Szretter Noste ME, Yohai VJ (2020) tauPFC: computes robust estimators for the PFC model. R package version 0.0.1. https:\u002F\u002Fgithub.com\u002Fmeszre\u002FtauPFC\nBoente G, Fraiman R (1989) Robust nonparametric regression estimation for dependent observations. Ann Stat 17(3):1242–1256\nBoente G, Martínez A (2017) Marginal integration m-estimators for additive models. TEST 26(2):231–260\nBura E, Cook RD (2001) Estimating the structural dimension of regressions via parametric inverse regression. J R Stat Soc Ser B (Stat Methodol) 63(2):393–410\nBura E, Cook RD (2003) Rank estimation in reduced-rank regression. J Multivar Anal 87(1):159–176\nBura E, Forzani L (2015) Sufficient reductions in regressions with elliptically contoured inverse predictors. J Am Stat Assoc 110(509):420–434\nBura E, Yang J (2011) Dimension estimation in sufficient dimension reduction: a unifying approach. J Multivar Anal 102(1):130–142\nBura E, Duarte S, Forzani L (2016) Sufficient reductions in regressions with exponential family inverse predictors. J Am Stat Assoc 111(515):1313–1329\nCook RD (2007) Fisher lecture: dimension reduction in regression. Stat Sci 22(1):1–26\nCook RD, Forzani L (2008) Principal fitted components for dimension reduction in regression. Stat Sci 23(4):485–501\nCook RD, Ni L (2005) Sufficient dimension reduction via inverse regression. J Am Stat Assoc 100(470):410–428\nCook RD, Weisberg S (1991) Comment. J Am Stat Assoc 86(414):328–332\nCook RD, Li B, Chiaromonte F (2010) Envelope models for parsimonious and efficient multivariate linear regression. Stat Sin 20:927–960\nCook RD, Forzani L, Tomassi D (2011) Ldr: a package for likelihood-based sufficient dimension reduction. J Stat Softw 39(1):1–20\nFilzmoser P, Dehon C, Croux C (2000) Outlier resistant estimators for canonical correlation analysis. In: COMPSTAT, Springer, pp 301–306\nGarcía Ben M, Martínez E, Yohai VJ (2006) Robust estimation for the multivariate linear model based on a \\(\\tau \\)-scale. J Multivar Anal 97(7):1600–1622\nGather U, Hilker T, Becker C (2001) A robustified version of sliced inverse regression. In: Statistics in genetics and in the environmental sciences, Springer, pp 147–157\nHampel FR (1971) A general qualitative definition of robustness. Ann Math Stat 42(6):1887–1896\nHastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn, Springer, New York,\nHuber PJ (1981) Robust statistics. Wiley, New York\nIzenman AJ (1975) Reduced-rank regression for the multivariate linear model. J Multivar Anal 5(2):248–264\nLi K-C (1991) Sliced inverse regression for dimension reduction. J Am Stat Assoc 86(414):316–327\nLi K-C (1992) On principal hessian directions for data visualization and dimension reduction: another application of stein’s lemma. J Am Stat Assoc 87(420):1025–1039\nLi B, Wang S (2007) On directional regression for dimension reduction. J Am Stat Assoc 102(479):997–1008\nLi B, Zha H, Chiaromonte F (2005) Contour regression: a general approach to dimension reduction. Ann Stat 33(4):1580–1616\nLi B, Artemiou A, Li L (2011) Principal support vector machines for linear and nonlinear sufficient dimension reduction. Ann Stat 39(6):3182–3210\nLopuhaä HP (1991) Multivariate \\(\\tau \\)-estimators for location and scatter. Can J Stat 19(3):307–321\nMaechler M, Rousseeuw P, Croux C, Todorov V, Ruckstuhl A, Salibian-Barrera M, Verbeke T, Koller M, Conceicao ELT, Anna di Palma M (2020) Robustbase: basic robust statistics. R package version 0.93-6\nMuler N, Yohai VJ (2002) Robust estimates for arch processes. J Time Ser Anal 23(3):341–375\nPapantoni-Kazakos P, Gray RM (1979) Robustness of estimators on stationary observations. Ann Probab 7(6):989–1002\nR Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria\nReinsel GC, Velu RP (1998) Multivariate reduced-rank regression: theory and applications. Springer, Berlin\nSalibian-Barrera M, Yohai VJ (2006) A fast algorithm for s-regression estimates. J Comput Gr Stat 15(2):414–427\nScrucca L (2011) Model-based sir for dimension reduction. Comput Stat Data Anal 55(11):3010–3026\nShe Y, Chen K (2017) Robust reduced-rank regression. Biometrika 104(3):633–647\nSzretter Noste ME (2019) Using dags to identify the sufficient dimension reduction in the principal fitted components model. Stat Probab Lett 145:317–320\nTatsuoka KS, Tyler DE (2000) On the uniqueness of s-functionals and m-functionals under nonelliptical distributions. Ann Stat 28(4):1219–1243\nTodorov V, Filzmoser P (2009) An object-oriented framework for robust multivariate analysis. J Stat Softw 32(3):1–47\nTyler DE (1987) A distribution-free m-estimator of multivariate scatter. Ann Stat 15:234–251\nWeisberg S (2005) Applied linear regression, vol 528. Wiley, New York\nYohai VJ (1987) High breakdown-point and high efficiency robust estimates for regression. Ann Stat 15(2):642–656\nYohai VJ, Zamar RH (1988) High breakdown-point estimates of regression by means of the minimization of an efficient scale. J Am Stat Assoc 83(402):406–413\nYohai VJ, Zamar RH (1997) Optimal locally robust m-estimates of regression. J Stat Plan Inference 64(2):309–323\nZhao W, Lian H, Ma S (2017) Robust reduced-rank modeling via rank regression. J Stat Plan Inference 180:1–12\nZhou J (2009) Robust dimension reduction based on canonical correlation. J Multivar Anal 100(1):195–209",{"EN":1177},"In nonparametric regression contexts, when the number of covariables is large, we face the curse of dimensionality. One way to deal with this problem when the sample is not large enough is using a reduced number of linear combinations of the explanatory variables that contain most of the information about the response variable. This leads to the so-called sufficient reduction problem. The purpose of this paper is to obtain robust estimators of a sufficient dimension reduction, that is, estimators which are not very much affected by the presence of a small fraction of outliers in the data. One way to derive a sufficient dimension reduction is by means of the principal fitted components (PFC) model. We obtain robust estimations for the parameters of this model and the corresponding sufficient dimension reduction based on a \n                \n                  \n                \n                $$\\tau $$\n                \n              -scale (\n                \n                  \n                \n                $$\\tau $$\n                \n              -estimators). Strong consistency of these estimators under weak assumptions of the underlying distribution is proven. The \n                \n                  \n                \n                $$\\tau $$\n                \n              -estimators for the PFC model are computed using an iterative algorithm. A Monte Carlo study compares the performance of \n                \n                  \n                \n                $$\\tau $$\n                \n              -estimators and maximum likelihood estimators. The results show clear advantages for \n                \n                  \n                \n                $$\\tau $$\n                \n              -estimators in the presence of outlier contamination and only small loss of efficiency when outliers are absent. A proposal to select the dimension of the reduction space based on cross-validation is given. These estimators are implemented in R language through functions contained in the package tauPFC. As the PFC model is a special case of multivariate reduced-rank regression, our proposal can be applied directly to this model as well.\n",{"EN":1179},"A robust proposal of estimation for the sufficient dimension reduction problem",{"VOID":1181},"10.1007\u002Fs11749-020-00745-9","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11749-020-00745-9",[1184,1199,1214],{"id":1185,"sortIndex":108,"researcher":21,"roles":1186,"affiliations":1187,"properties":1196},"bedd634a-6dad-4d3d-840f-6f8148e816b0",[139],[1188],{"id":21,"sortIndex":22,"affiliation":1189,"properties":21},{"id":1190,"createTime":1191,"updateTime":1191,"relativeEntities":1192,"slug":21,"properties":1193,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"429beefb-17e1-46ad-a5ee-1b71907803eb","2024-01-27T02:51:22.953+00:00",[],{"title":1194},{"VI":1195},"Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina",{"title":1197},{"VI":1198},"María Eugenia Szretter Noste",{"id":1200,"sortIndex":22,"researcher":21,"roles":1201,"affiliations":1202,"properties":1211},"9b8f6d64-f660-416e-bb13-b1a63c9772f7",[139],[1203],{"id":21,"sortIndex":22,"affiliation":1204,"properties":21},{"id":1205,"createTime":1206,"updateTime":1206,"relativeEntities":1207,"slug":21,"properties":1208,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"23fe2cab-b15a-4878-9d9b-b22b30bbd902","2024-01-09T23:49:07.065+00:00",[],{"title":1209},{"VI":1210},"Departamento de Matemática, Facultad de Ingeniería Química, Universidad Nacional del Litoral, Santa Fe, Argentina",{"title":1212},{"VI":1213},"Andrea Bergesio",{"id":1215,"sortIndex":107,"researcher":21,"roles":1216,"affiliations":1217,"properties":1245},"4f55d493-4171-4511-8839-0d873842eccc",[139],[1218,1223,1233],{"id":21,"sortIndex":22,"affiliation":1219,"properties":21},{"id":1190,"createTime":1191,"updateTime":1191,"relativeEntities":1220,"slug":21,"properties":1221,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},[],{"title":1222},{"VI":1195},{"id":1224,"sortIndex":108,"affiliation":1225,"properties":1232},"0c56bacf-3737-4995-99c7-ea0386698aed",{"id":1226,"createTime":1227,"updateTime":1227,"relativeEntities":1228,"slug":21,"properties":1229,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"93d9fec0-181c-430a-8420-3968067b6023","2023-12-19T19:55:57.280+00:00",[],{"title":1230},{"VI":1231},"Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina",{},{"id":1234,"sortIndex":107,"affiliation":1235,"properties":1244},"11e987a6-4afd-418b-b8c8-ed511498c11c",{"id":1236,"createTime":1237,"updateTime":1238,"relativeEntities":1239,"slug":1240,"properties":1241,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"160a90ee-a2bf-4f3f-a5be-dcf4f6ca7c8b","2023-12-26T02:05:29.977+00:00","2024-10-10T06:04:55.374+00:00",[],"Consejo-Nacional-de-Investigaciones-Cient%C3%ADficas-y-T%C3%A9cnicas-CONICET-Buenos-Aires-Argentina",{"title":1242},{"VI":1243},"Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina",{},{"title":1246},{"VI":1247},"Víctor J. Yohai",{"url":1182,"publisher":1249,"properties":1278},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1250,"slug":10,"properties":1251,"entityType":19,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"subjectFields":1256,"manageAffiliations":1257,"indexDatabases":1258,"url":21,"thumbnailPath":21,"statistic":1273,"gsStatistic":21,"type":111,"analyzePriority":21},[],{"issn":1252,"eissn":1253,"title":1254,"url":1255},{"VOID":13},{"VOID":15},{"EN":10},{"VOID":18},[],[],[1259,1266],{"id":65,"indexDatabase":1260,"url":78,"indexYears":79,"academicFieldIds":1265,"indexDatabaseRanking":83},{"id":67,"createTime":68,"updateTime":69,"relativeEntities":1261,"label":1262,"description":1263,"key":75,"publicationTags":1264,"standard":21},[],{"EN":72,"VI":72},{"EN":72,"VI":74},[77],[81,82],{"id":85,"indexDatabase":1267,"url":100,"indexYears":21,"academicFieldIds":1272,"indexDatabaseRanking":21},{"id":87,"createTime":88,"updateTime":89,"relativeEntities":1268,"label":1269,"description":1270,"key":96,"publicationTags":1271,"standard":21},[],{"EN":92,"VI":92},{"VI":94,"EN":95},[98,99],[102],{"impactFactor":22,"impactFactorByYear":1274,"i10Index":22,"i10IndexLast5Year":22,"totalPublication":105,"totalPublicationByYear":1275,"totalCitation":22,"totalCitationByYear":1276,"totalCitationPerPublication":22,"totalCitationPerPublicationByYear":1277,"hindexLast5Year":22,"hindex":22},{},{"2019":107,"2020":108,"2021":108},{},{},{"volume":1279,"pages":1281},{"VOID":1280},"30",{"VOID":1282},"758-783","2021-01-03",2021,{"id":1286,"createTime":1287,"updateTime":1288,"relativeEntities":1289,"slug":1290,"properties":1291,"entityType":131,"verifyStatus":132,"verifyTime":1288,"verifyNote":133,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"primaryUrl":1300,"fullTextUrl":21,"authors":1301,"publicationType":169,"publisherRelationship":1317,"citationCount":21,"citationInfo":21,"publishDate":1352,"publishYear":1353,"citationAnalyzeStatus":20,"lastCitationAnalyze":21,"indexDatabases":21,"openAccess":21,"references":21,"isForceReanalyzing":207},"f0bf8f73-c2d5-48b4-9dfc-9b154bee9f74","2024-01-08T19:47:21.627+00:00","2025-01-18T23:35:37.275+00:00",[],"Reweighted-least-trimmed-squares-an-alternative-to-one-step-estimators",{"references":1292,"abstract":1294,"title":1296,"doi":1298},{"VOID":1293},"Aquaro M, Čížek P (2010) Two-step robust estimation of fixed-effects panel data models. CentER discussion paper, Tilburg University\nBalke NS, Fomby TB (1994) Large shocks, small shocks, and economic fluctuations: outliers in macroeconomic time series. J Appl Econom 9:181–200\nBaltagi BH, Jung BC, Song SH (2010) Testing for heteroskedasticity and serial correlation in a random effects panel data model. J Econom 154:122–124\nChen L-A, Chiang Y-C (1996) Symmetric quantile and symmetric trimmed mean for linear regression model. J Nonparametr Stat 7:171–185\nČížek P (2006) Least trimmed squares under dependence. J Stat Plan Inference 136:3967–3988\nČížek P (2008) General trimmed estimation: robust approach to nonlinear and limited dependent variable models. Econom Theory 24:1500–1529\nČížek P (2010) Reweighted least trimmed squares: an alternative to one-step estimators. CentER discussion paper 2010\u002F91, Tilburg University, The Netherlands\nČížek P (2011) Efficient robust estimation of regression models. Comput Stat Data Anal 55:774–788\nDavidson J (1994) Stochastic limit theory. Oxford University Press, New York\nDavies PL, Gather U (2005) Breakdown and groups. Ann Stat 33:977–1035\nDe Long JB, Summers LH (1991) Equipment investment and economic growth. Q J Econ 106:445–501\nEngler E, Nielsen B (2009) The empirical process of autoregressive residuals. Econom J 12(2):367–381\nGenton MG, Lucas A (2003) Comprehensive definitions of breakdown points for independent and dependent observations. J R Stat Soc, Ser B 65:81–94\nGervini D, Yohai VJ (2002) A class of robust and fully efficient regression estimators. Ann Stat 30:583–616\nHampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA (1986) Robust statistics: the approach based on influence function. Wiley, New York\nHe X, Portnoy S (1992) Reweighted LS estimators converge at the same rate as the initial estimator. Ann Stat 20:2161–2167\nKoenker RW, Bassett G (1978) Regression quantiles. Econometrica 46:33–50\nMarazzi A, Yohai VJ (2004) Adaptively truncated maximum likelihood regression with asymmetric errors. J Stat Plan Inference 122:271–291\nPreminger A, Franck R (2007) Foreign exchange rates: a robust regression approach. Int J Forecast 23:71–84\nRonchetti E, Trojani F (2001) Robust inference with GMM estimators. J Econom 101:37–69\nRousseeuw PJ (1984) Least median of squares regression. J Am Stat Assoc 79:871–880\nRousseeuw PJ (1985) Multivariate estimation with high breakdown point. In: Grossman W, Pflug G, Vincze I, Wertz W (eds) Mathematical statistics and applications, vol B. Reidel, Dordrecht, pp 283–297\nRousseeuw PJ, Leroy AM (1987) Robust regression and outlier detection. Wiley, New York\nRousseeuw PJ, Yohai VJ (1984) Robust regression by means of S-estimators. In: Franke J, Härdle W, Martin RD (eds) Robust and nonlinear time series analysis. Lecture notes in statistics, vol 26. Springer, New York, pp 256–272\nRuppert D, Carroll RJ (1980) Trimmed least squares estimation in the linear model. J Am Stat Assoc 75:828–838\nSakata S, White H (1998) High breakdown point conditional dispersion estimation with application to S&P 500 daily returns volatility. Econometrica 66:529–567\nSimpson DG, Ruppert D, Carroll RJ (1992) On one-step GM estimates and stability of inferences in linear regression. J Am Stat Assoc 87:439–450\nStromberg AJ, Hössjer O, Hawkins DM (2000) The least trimmed difference regression estimator and alternatives. J Am Stat Assoc 95:853–864\nTableman M (1994) The influence functions for the least trimmed squares and the least trimmed absolute deviations estimators. Stat Probab Lett 19:329–337\nTemple JRW (1998) Robustness tests of the augmented Solow model. J Appl Econom 13:361–375\nVíšek JÁ (2002) The least weighted squares I. The asymptotic linearity of normal equations. Bull Czech Econom Soc 9(15):31–58\nWelsh AH, Ronchetti E (2002) A journey in single steps: robust one-step M-estimation in linear regression. J Stat Plan Inference 103:287–310\nWoo J (2003) Economic, political, and institutional determinants of public deficits. J Public Econ 87:387–426\nZaman A, Rousseeuw PJ, Orhan M (2001) Econometric applications of high-breakdown robust regression techniques. Econ Lett 71:1–8",{"EN":1295},"A new class of robust regression estimators is proposed that forms an alternative to traditional robust one-step estimators and that achieves the \n                  \n                    \n                  \n                  $\\sqrt{n}$\n                 rate of convergence irrespective of the initial estimator under a wide range of distributional assumptions. The proposed reweighted least trimmed squares (RLTS) estimator employs data-dependent weights determined from an initial robust fit. Just like many existing one- and two-step robust methods, the RLTS estimator preserves robust properties of the initial robust estimate. However contrary to existing methods, the first-order asymptotic behavior of RLTS is independent of the initial estimate even if errors exhibit heteroscedasticity, asymmetry, or serial correlation. Moreover, we derive the asymptotic distribution of RLTS and show that it is asymptotically efficient for normally distributed errors. A simulation study documents benefits of these theoretical properties in finite samples.",{"EN":1297},"Reweighted least trimmed squares: an alternative to one-step estimators",{"VOID":1299},"10.1007\u002Fs11749-013-0335-5","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11749-013-0335-5",[1302],{"id":1303,"sortIndex":22,"researcher":21,"roles":1304,"affiliations":1305,"properties":1314},"a72eaf16-cc2e-469e-a636-72e1f2e63960",[139],[1306],{"id":21,"sortIndex":22,"affiliation":1307,"properties":21},{"id":1308,"createTime":1309,"updateTime":1309,"relativeEntities":1310,"slug":21,"properties":1311,"entityType":49,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22},"d1222142-cfde-46ec-a360-6c865bc928b7","2024-01-08T19:47:21.634+00:00",[],{"title":1312},{"VI":1313},"CentER, Department of Econometrics & OR, Tilburg University, Tilburg, The Netherlands",{"title":1315},{"VI":1316},"Pavel Čížek",{"url":1300,"publisher":1318,"properties":1347},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1319,"slug":10,"properties":1320,"entityType":19,"verifyStatus":20,"verifyTime":21,"verifyNote":21,"syncStatus":20,"languages":21,"translateLanguages":21,"viewCount":22,"subjectFields":1325,"manageAffiliations":1326,"indexDatabases":1327,"url":21,"thumbnailPath":21,"statistic":1342,"gsStatistic":21,"type":111,"analyzePriority":21},[],{"issn":1321,"eissn":1322,"title":1323,"url":1324},{"VOID":13},{"VOID":15},{"EN":10},{"VOID":18},[],[],[1328,1335],{"id":65,"indexDatabase":1329,"url":78,"indexYears":79,"academicFieldIds":1334,"indexDatabaseRanking":83},{"id":67,"createTime":68,"updateTime":69,"relativeEntities":1330,"label":1331,"description":1332,"key":75,"publicationTags":1333,"standard":21},[],{"EN":72,"VI":72},{"EN":72,"VI":74},[77],[81,82],{"id":85,"indexDatabase":1336,"url":100,"indexYears":21,"academicFieldIds":1341,"indexDatabaseRanking":21},{"id":87,"createTime":88,"updateTime":89,"relativeEntities":1337,"label":1338,"description":1339,"key":96,"publicationTags":1340,"standard":21},[],{"EN":92,"VI":92},{"VI":94,"EN":95},[98,99],[102],{"impactFactor":22,"impactFactorByYear":1343,"i10Index":22,"i10IndexLast5Year":22,"totalPublication":105,"totalPublicationByYear":1344,"totalCitation":22,"totalCitationByYear":1345,"totalCitationPerPublication":22,"totalCitationPerPublicationByYear":1346,"hindexLast5Year":22,"hindex":22},{},{"2019":107,"2020":108,"2021":108},{},{},{"volume":1348,"pages":1350},{"VOID":1349},"22",{"VOID":1351},"514-533","2013-07-04",2013]