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In particular, an interpretation of the fiducial argument is defended in which fiducial probability is treated as being subjective and the role taken by pivots in a more standard interpretation is taken by what are called primary random variables, which in fact form a special class of pivots. The resulting methodology, which is referred to as subjective fiducial inference, is outlined in the first part of the paper. This is followed by a defence of this methodology arranged in a series of criticisms and responses. These criticisms reflect objections that are often raised against standard fiducial inference and incorporate more specific concerns that are likely to exist with respect to subjective fiducial inference. It is hoped that the responses to these criticisms clarify the contribution that a system of fiducial reasoning can make to statistical inference.",{"EN":261},"A defence of subjective fiducial inference",{"VOID":263},"[]",{"VOID":265},"Barnard, G.A.: R. A. 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B 30, 205–247 (1968)\nEdwards, A.W.F.: Fiducial probability. Statistician 25, 15–35 (1976)\nEfron, B.: R. A. Fisher in the 21st Century (with discussion). Stat. Sci. 13, 95–122 (1998)\nde Finetti, B.: Theory of probability, vol. 1. Wiley, Chichester (1974)\nde Finetti, B.: Theory of probability, vol. 2. Wiley, Chichester (1975)\nFine, T.L.: Theories of probability: an examination of foundations. Academic Press, New York (1973)\nFisher, R.A.: Inverse probability. Math. Proceed. Cambridge Philosop. Soc. 26, 528–535 (1930)\nFisher, R.A.: Statistical methods and scientific inference, 1st edn. Hafner Press, New York (1956) [2nd edn., 1959; 3rd edn., 1973]\nFraser, D.A.S.: On fiducial inference. Ann. Math. Stat. 32, 661–676 (1961)\nFraser, D.A.S.: Structural probability and a generalization. Biometrika 53, 1–9 (1966)\nFraser, D.A.S.: Events, information processing, and the structured model. In: Godambe, V.P., Sprott, D.A. (eds.) Foundations of Statistical Inference, pp. 32–55. Holt, Renehart and Winston, Toronto (1972)\nGhosh, M.: Objective priors: an introduction for frequentists (with discussion). Stat. Sci. 26, 187–211 (2011)\nGood, I.J.: Kinds of probability. Science 129, 443–447 (1959)\nGood, I.J.: The interface between statistics and the philosophy of science (with discussion). Stat. Sci. 3, 386–412 (1988)\nGoutis, C., Casella, G.: Frequentist post-data inference. Int. Stat. Rev. 63, 325–344 (1995)\nHannig, J.: On generalized fiducial inference. Stat. Sinica 19, 491–544 (2009)\nHannig, J., Iyer, H., Patterson, P.: Fiducial generalized confidence intervals. J. Am. Stat. Assoc. 101, 254–269 (2006)\nJeffreys, H.: Theory of probability, 3rd edn. Oxford University Press, Oxford (1961)\nKass, R.E., Wasserman, L.: The selection of prior distributions by formal rules. J. Am. Stat. Assoc. 91, 1343–1370 (1996)\nLindley, D.: Some comments on ‘Non-informative priors do not exist’. J. Stat. Plan. Infer. 65, 182–184 (1997)\nPedersen, J.G.: Fiducial inference. Int. Stat. Rev. 46, 147–170 (1978)\nRobinson, G.K.: Conditional properties of statistical procedures. Ann. Stat. 7, 742–755 (1979a)\nRobinson, G.K.: Conditional properties of statistical procedures for location and scale families. Ann. Stat. 7, 756–771 (1979b)\nSavage, L.J.: The foundations of statistics. Wiley, New York (1954)\nSavage, L.J.: The foundations of statistics reconsidered. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1., pp. 575–586, University of California Press, Berkeley (1961)\nSeidenfeld, T.: Why I am not an objective Bayesian; some reflections prompted by Rosenkrantz. Theory Decision 11, 413–440 (1979)\nShafer, G.: The unity and diversity of probability (with discussion). Stat Sci 5, 435–462 (1990)\nSpetzler, C.S., Stael von Holstein, C.A.S.: Probability encoding in decision analysis. Manag. Sci. 22, 340–358 (1975)\nStone, M.: Strong inconsistency from uniform priors (with discussion). J. Am. Stat. 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The model involves a Gaussian Field (GF), affected by a measurement error, and a state process characterized by a first order autoregressive dynamic model and spatially correlated innovations. This kind of model is well discussed and widely used in the air quality literature thanks to its flexibility in modelling the effect of relevant covariates (i.e. meteorological and geographical variables) as well as time and space dependence. However, Bayesian inference—through Markov chain Monte Carlo (MCMC) techniques—can be a challenge due to convergence problems and heavy computational loads. In particular, the computational issue refers to the infeasibility of linear algebra operations involving the big dense covariance matrices which occur when large spatio-temporal datasets are present. The main goal of this work is to present an effective estimating and spatial prediction strategy for the considered spatio-temporal model. This proposal consists in representing a GF with Matérn covariance function as a Gaussian Markov Random Field (GMRF) through the Stochastic Partial Differential Equations (SPDE) approach. The main advantage of moving from a GF to a GMRF stems from the good computational properties that the latter enjoys. In fact, GMRFs are defined by sparse matrices that allow for computationally effective numerical methods. Moreover, when dealing with Bayesian inference for GMRFs, it is possible to adopt the Integrated Nested Laplace Approximation (INLA) algorithm as an alternative to MCMC methods giving rise to additional computational advantages. The implementation of the SPDE approach through the R-library INLA (\n                  www.r-inla.org\n                  \n                ) is illustrated with reference to the Piemonte PM data. In particular, providing the step-by-step R-code, we show how it is easy to get prediction and probability of exceedance maps in a reasonable computing time.",{"EN":369},"Spatio-temporal modeling of particulate matter concentration through the SPDE 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S., Carlin, B., Gelfand, A.: Hierarchical Modeling and Analysis for Spatial Data. Monographs on Statistics and Applied Probability. Chapman and Hall, New York (2004)",{},{"id":524,"text":525,"url":526,"identifiers":527},"4c68646b-0035-4279-8000-0006b275d4fa","Banerjee, S., Gelfand, A., Finley, A., Sang, H.: Gaussian predictive process models for large spatial datasets. J. R. Stat. Soc. B 70(4), 825–848 (2008)","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":528},"10.1007\u002Fs10440-022-00541-7",{"id":22,"text":530,"url":22,"identifiers":531},"Besag, J.: Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Stat. Soc. B 36(2), 192–225 (1974)",{},{"id":22,"text":533,"url":534,"identifiers":535},"Cameletti, M., Ignaccolo, R., Bande, S.: Comparing spatio-temporal models for particulate matter in Piemonte. Environmetrics (2011). doi:10.1002\u002Fenv.1139","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fenv.1139",{"mag":536,"openalex":537,"doi":538},"1870676938","W1870676938","10.1002\u002Fenv.1139",{"id":540,"text":541,"url":542,"identifiers":543},"bec2403a-d5f2-4b1f-a774-95ffa0d7c268","Cocchi, D., Greco, F., Trivisano, C.: Hierarchical space-time modelling of PM10 pollution. Atmos. Environ. 41, 532–542 (2007)","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1352231006008545",{"doi":544},"10.1016\u002Fj.atmosenv.2006.08.032",{"id":524,"text":546,"url":526,"identifiers":547},"Cressie, N.: Statistics for Spatial Data. Wiley, New York (1993)",{"doi":528},{"id":22,"text":549,"url":22,"identifiers":550},"Cressie, N., Johannesson, G.: Fixed rank kriging for large spatial datasets. J. R. Stat. Soc. B 70, 209–226 (2008)",{},{"id":524,"text":552,"url":526,"identifiers":553},"Cressie, N., Wikle, C.: Statistics for Spatio-Temporal Data. Wiley, New York (2011)",{"doi":528},{"id":524,"text":555,"url":526,"identifiers":556},"Fassò, A., Finazzi, F.: Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data. Environmetrics 22, 735–748 (2011)",{"doi":528},{"id":524,"text":558,"url":526,"identifiers":559},"Finardi, S., De Maria, R., D’Allura, A., Cascone, C., Calori, G., Lollobrigida, F.: A deterministic air quality forecasting system for Torino urban area, Italy. Environ. Model. Softw. 23(3), 344–355 (2008)",{"doi":528},{"id":524,"text":561,"url":526,"identifiers":562},"Furrer, R., Genton, M., Nychka, D.: Covariance tapering for interpolation of large spatial datasets. J. Comput. Graph. Stat. 15(3), 502–523 (2006)",{"doi":528},{"id":22,"text":564,"url":22,"identifiers":565},"Gelfand, A., Diggle, P., Fuentes, M., Guttorp, P. (eds.): Handbook of Spatial Statistics. Chapman & Hall, New York (2010)",{},{"id":524,"text":567,"url":526,"identifiers":568},"Lindgren, F., Rue, H., Lindström, J.: An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach (with discussion). J. R. Stat. Soc. B 73(4), 423–498 (2011)",{"doi":528},{"id":524,"text":570,"url":526,"identifiers":571},"R Development Core Team: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2011). URL http:\u002F\u002Fwww.R-project.org, ISBN 3-900051-07-0",{"doi":528},{"id":524,"text":573,"url":526,"identifiers":574},"Rue, H., Held, L.: Gaussian Markov Random Fields. Theory and Applications. Chapman & Hall, New York (2005)",{"doi":528},{"id":524,"text":576,"url":526,"identifiers":577},"Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian model by using integrated nested Laplace approximations (with discussion). J. R. Stat. Soc. B 71, 319–392 (2009)",{"doi":528},{"id":22,"text":579,"url":22,"identifiers":580},"Sahu, S.: Hierarchical Bayesian Models for Space-time Air Pollution Data. Rao, C. (ed.): Handbook of Statistics—Time Series Analysis, Methods and Applications. Handbook of Statistics, vol. 30. Elsevier, Amsterdam (2011)",{},{"id":524,"text":582,"url":526,"identifiers":583},"Samet, J., Dominici, F., Curriero, F., Coursac, I., Zeger, S.: Fine particulate air pollution and mortality in 20 US cities: 1987–1994. N. Engl. J. Med. 343, 1742–1749 (2000)",{"doi":528},{"id":524,"text":585,"url":526,"identifiers":586},"Samoli, E., Peng, R., Ramsay, T., Pipikou, M., Touloumi, G., Dominici, F., Burnett, R., Cohen, A., Krewski, D., Samet, J., Katsouyanni, K.: Acute effects of ambient particulate matter on mortality in Europe and North America: results from the APHENA study. Environ. Health Perspect. 116, 1480–1486 (2008)",{"doi":528},{"id":588,"createTime":589,"updateTime":590,"relativeEntities":591,"slug":592,"properties":593,"entityType":154,"verifyStatus":155,"verifyTime":602,"verifyNote":157,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":603,"fullTextUrl":22,"authors":604,"publicationType":176,"publisherRelationship":622,"citationCount":124,"citationInfo":689,"publishDate":691,"publishYear":246,"citationAnalyzeStatus":516,"lastCitationAnalyze":692,"indexDatabases":693,"openAccess":22,"references":694,"isForceReanalyzing":250},"9fbd3f8a-4661-4835-8014-13340d00932d","2024-01-01T10:48:05.607+00:00","2026-07-15T09:04:51.884+00:00",[],"Bootstrapping-a-hedonic-price-index-experience-from-used-cars-data",{"abstract":594,"title":596,"gsPaper":598,"doi":600},{"EN":595},"Every hedonic price index is an estimate of an unknown economic parameter. It depends, in practice,\n   on one or more random samples of prices and characteristics of a certain good. Bootstrap resampling\n   methods provide a tool for quantifying sampling errors. Following some general reflections on hedonic\n   elementary price indices, this paper proposes a case-based, a model-based, and a wild bootstrap\n   approach for estimating confidence intervals for hedonic price indices. Empirical results are obtained\n   for a data set on used cars in Switzerland. A simple and an enhanced adaptive semi-logarithmic\n   model are fit to monthly samples, and bootstrap confidence intervals are estimated for Jevons-type hedonic\n  elementary price indices.\n ",{"EN":597},"Bootstrapping a hedonic price index: experience from used cars data",{"VOID":599},"[\"2606632720586085690\"]",{"VOID":601},"10.1007\u002Fs10182-006-0015-9","2024-04-28T06:38:30.654+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10182-006-0015-9",[605],{"id":606,"sortIndex":23,"researcher":22,"roles":607,"affiliations":608,"properties":617,"displayName":619,"givenName":22,"familyName":22},"e65ba3ab-2924-4bcd-93aa-cfc34e48df83",[163],[609],{"id":610,"sortIndex":23,"affiliation":611,"properties":22},"5a13b97d-8e94-480f-8be7-611b20e4f2d6",{"id":610,"createTime":22,"updateTime":22,"relativeEntities":612,"slug":22,"properties":613,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":616,"statistic":22},[],{"title":614},{"VI":615},"Seminar of Statistics, Dept. of Quantitative Economics, University of Fribourg Switzerland, Fribourg, Switzerland",[],{"title":618,"gsAuthor":620},{"VI":619},"Michael Beer",{"VOID":621},"[\"GmyPT8YAAAAJ\"]",{"url":603,"publisher":623,"properties":685},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":624,"slug":10,"properties":625,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":629,"manageAffiliations":654,"indexDatabases":665,"url":22,"thumbnailPath":22,"statistic":680,"gsStatistic":22,"type":136,"analyzePriority":22},[],{"issn":626,"title":627,"eissn":628},{"VOID":15},{"EN":17},{"VOID":13},[630,634,638,642,646,650],{"id":44,"createTime":22,"updateTime":22,"relativeEntities":631,"label":632,"description":633,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":47},{},{"id":26,"createTime":22,"updateTime":22,"relativeEntities":635,"label":636,"description":637,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":29},{},{"id":32,"createTime":22,"updateTime":22,"relativeEntities":639,"label":640,"description":641,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":35},{},{"id":38,"createTime":22,"updateTime":22,"relativeEntities":643,"label":644,"description":645,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":41},{},{"id":50,"createTime":22,"updateTime":22,"relativeEntities":647,"label":648,"description":649,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":53},{},{"id":56,"createTime":22,"updateTime":22,"relativeEntities":651,"label":652,"description":653,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":59},{},[655,660],{"id":63,"createTime":22,"updateTime":22,"relativeEntities":656,"slug":22,"properties":657,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":659,"statistic":22},[],{"title":658},{"EN":67},[69],{"id":71,"createTime":22,"updateTime":22,"relativeEntities":661,"slug":22,"properties":662,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":664,"statistic":22},[],{"title":663},{"EN":75},[],[666,673],{"id":79,"indexDatabase":667,"url":92,"indexYears":22,"academicFieldIds":672,"indexDatabaseRanking":22},{"id":81,"createTime":22,"updateTime":22,"relativeEntities":668,"label":669,"description":670,"key":88,"publicationTags":671,"standard":22},[],{"EN":84,"VI":84},{"EN":86,"VI":87},[90,91],[94],{"id":96,"indexDatabase":674,"url":107,"indexYears":108,"academicFieldIds":679,"indexDatabaseRanking":116},{"id":98,"createTime":22,"updateTime":22,"relativeEntities":675,"label":676,"description":677,"key":104,"publicationTags":678,"standard":22},[],{"EN":101,"VI":101},{"EN":101,"VI":103},[106],[110,111,112,113,114,115],{"impactFactor":23,"impactFactorByYear":681,"i10Index":23,"i10IndexLast5Year":23,"totalPublication":119,"totalPublicationByYear":682,"totalCitation":23,"totalCitationByYear":683,"totalCitationPerPublication":23,"totalCitationPerPublicationByYear":684,"hindexLast5Year":23,"hindex":23},{},{"2007":121,"2008":122,"2009":123,"2010":124,"2011":125,"2012":126,"2013":124,"2014":123,"2015":127,"2016":128,"2017":129,"2018":130,"2019":126,"2020":131,"2021":132,"2022":130,"2023":127,"2024":133},{},{},{"pages":686,"volume":688},{"VOID":687},"77-92",{"VOID":244},{"total":124,"publishYear":246,"statisticByYear":690},{"2009":396,"2011":396,"2013":396,"2014":396,"2015":396,"2017":414,"2018":414,"2021":396,"2022":396},"2007-01-18","2026-07-15T09:04:51.883+00:00",[90],[695,698,701,704,707,710,713,716,719,722,725,728,731,734,737,740,743],{"id":22,"text":696,"url":22,"identifiers":697},"Brachinger, H.W. (2002) Statistical Theory of Hedonic Price Indices. Working paper 1, Department of Quantitative Economics, University of Fribourg, Switzerland",{},{"id":22,"text":699,"url":22,"identifiers":700},"Court, A.T. (1939) Hedonic price indexes with automotive examples. In: The Dynamics of Automobile Demand, pp. 99–117. General Motors Corporation, New York",{},{"id":524,"text":702,"url":526,"identifiers":703},"Curry, B., Morgan, P., Silver, M. (2001) Hedonic regressions: Mis-specification and neural networks. Applied Economics 33, 659–671",{"doi":528},{"id":22,"text":705,"url":22,"identifiers":706},"Davidson, R., Flachaire, E. (2001) The Wild Bootstrap, Tamed at Last. IER Working paper 1000, Queen’s Institute for Economic Research, Ontario",{},{"id":22,"text":708,"url":22,"identifiers":709},"Davison, A.C., Hinkley, D.V. (1997) Bootstrap methods and their application. Cambridge University Press, Cambridge",{},{"id":524,"text":711,"url":526,"identifiers":712},"Hulten, C.R. (2003) Price hedonics: a critical review. Economic Policy Review 9, 5–15",{"doi":528},{"id":524,"text":714,"url":526,"identifiers":715},"ILO, IMF, OECD, UNECE, Eurostat, The World Bank (eds.) (2004) Consumer Price Index Manual: Theory and Practice. International Labour Office, Geneva",{"doi":528},{"id":524,"text":717,"url":526,"identifiers":718},"Liu, R.Y. (1988) Bootstrap procedures under some non-I.I.D. models. Annals of Statistics 16, 1696–1708",{"doi":528},{"id":524,"text":720,"url":526,"identifiers":721},"MacKinnon, J.G. (2002) Bootstrap inference in econometrics. Canadian Journal of Economics 35, 615–645",{"doi":528},{"id":524,"text":723,"url":526,"identifiers":724},"Mammen, E. (1993) Bootstrap and wild bootstrap for high dimensional linear models. The Annals of Statistics 21, 255–285",{"doi":528},{"id":524,"text":726,"url":526,"identifiers":727},"Murray, J., Sarantis, N. (1999) Price-quality relations and hedonic price indexes for cars in the United Kingdom. International Journal of the Economics of Business 6, 5–27",{"doi":528},{"id":524,"text":729,"url":526,"identifiers":730},"Pakes, A. (2003) A reconsideration of hedonic price indexes with an application to PC’s. American Economic Review 93, 1578–1596",{"doi":528},{"id":524,"text":732,"url":526,"identifiers":733},"R Development Core Team (2005) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna",{"doi":528},{"id":22,"text":735,"url":22,"identifiers":736},"Reis, H.J., Santos Silva, J.M.C. (2002) Hedonic Price Indexes for New Passenger Cars in Portugal (1997–2001). Working paper 10-02, Banco de Portugal Economic Research Department, Lisboa",{},{"id":524,"text":738,"url":526,"identifiers":739},"Triplett, J. (2004) Handbook on Hedonic Indexes and Quality Adjustments in Price Indexes: Special Application to Information Technology Products. OECD Science, Technology and Industry Working Paper 2004\u002F9, OECD Publishing, Paris",{"doi":528},{"id":22,"text":741,"url":22,"identifiers":742},"Venables, W.N., Ripley, B.D. (2002) Modern Applied Statistics with S. Springer, New York",{},{"id":22,"text":744,"url":22,"identifiers":745},"Yu, K. (2003) An Elementary Price Index for Internet Service Providers in Canada: A Hedonic Study. Working paper, Department of Economics, Lakehead University, Thunder Bay, Ontario",{},{"id":747,"createTime":748,"updateTime":749,"relativeEntities":750,"slug":751,"properties":752,"entityType":154,"verifyStatus":155,"verifyTime":762,"verifyNote":157,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":763,"fullTextUrl":22,"authors":764,"publicationType":176,"publisherRelationship":793,"citationCount":22,"citationInfo":22,"publishDate":861,"publishYear":862,"citationAnalyzeStatus":356,"lastCitationAnalyze":863,"indexDatabases":864,"openAccess":22,"references":22,"isForceReanalyzing":250},"5016a4b5-797c-4a96-8127-e29aa59a45ba","2024-01-25T12:20:26.903+00:00","2026-06-13T13:40:39.671+00:00",[],"Prediction-based-estimating-functions-for-stochastic-volatility-models-with-noisy-data-comparison-with-a-GMM-alternative",{"abstract":753,"title":755,"gsPaper":757,"references":758,"doi":760},{"EN":754},"Prediction-based estimating functions (PBEFs), introduced in Sørensen (Econom J 3:123–147, 2000), are reviewed, and PBEFs for the Heston (Rev Financ Stud 6:327–343, 1993) stochastic volatility model are derived with and without the inclusion of noise in the data. The finite sample performance of the PBEF-based estimator is investigated in a Monte Carlo study and compared to the performance of the Generalized Method of Moments (GMM) estimator from Bollerslev and Zhou (J Econom 109:33–65, 2002) that is based on conditional moments of integrated variance. We derive new moment conditions in the presence of noise, but we also consider noise correcting the GMM estimator by basing it on a realized kernel instead of realized variance. Our Monte Carlo study reveals great promise for the estimator based on PBEFs. The study also shows that the PBEF-based estimator outperforms the GMM estimator, both in the setting with MMS noise and in the setting without MMS noise, especially for small sample sizes. Finally, in an empirical application we fit the Heston model to SPY data and investigate how the two methods handle real data and possible model misspecification. The empirical study also shows how the flexibility of the PBEF-based method can be used for robustness checks.",{"EN":756},"Prediction-based estimating functions for stochastic volatility models with noisy data: comparison with a GMM alternative",{"VOID":263},{"VOID":759},"Andersen, T.G., Bollerslev, T.: Intraday periodicity and volatility persistence in financial markets. J. Empir. Financ. 4, 115–158 (1997)\nAndersen, T.G., Davis, R., Kreiss, J.-P., Mikosch, T.: Handbook of Financial Time Series. Springer, Berlin (2009)\nAnt-Sahalia, Y., Kimmel, R.: Maximum likelihood estimation of stochastic volatility models. J. Financ. Econ. 83, 413–452 (2007)\nBarndorff-Nielsen, O.E., Hansen, P.R., Lunde, A., Shephard, N.: Designing realized kernels to meausure the ex post variantion of equity prices in the presence of noise. Econometrica 76, 1481–1536 (2008a)\nBarndorff-Nielsen, O.E., Hansen, P.R., Lunde, A., Shephard, N.: Realised kernels in practice: trades and quotes. Econom. J. 04, 1–32 (2008b)\nBarndorff-Nielsen, O.E., Shephard, N.: Non-Gaussian OU-based models and some of their uses in financial economics (with dicussion). J. R. Stat. Soc. B 63, 167–241 (2001)\nBarndorff-Nielsen, O.E., Shephard, N.: Econometric analysis of realized volatility and its use in estimating stochastic volatility models. J. R. Stat. Soc. B 64, 253–280 (2002)\nBollerslev, T., Zhou, H.: Estimating stochastic volatility diffusions using conditional moments of integrated volatility. J. Econom. 109, 33–65 (2002)\nBradley, R.C.: Basic properties of strong mixing conditions: a survey and some open questions. Probab. Surv. 2, 107–144 (2005)\nBrockwell, P.J.: LTvy-driven CARMA processes. Ann. Inst. Statist. Math. 53, 113–124 (2001)\nCorradi, V., Distaso, W.: Semi-parametric comparison of stochastic volatility models using realized measures. Rev. Econ. Stud. 73, 635–667 (2006)\nDacorogna, M., Mnller, U., Nagler, R., Olsen, R., Pictet, O.: A geographical model for the daily and weekly seasonal volatility in the foreign exchange market. J. Int. Money Financ. 12, 413–438 (1993)\nEraker, B.: Markov Chain Monte Carlo analysis of diffusion models with application to finance. J. Bus. Econ. Stat. 19–2, 177–191 (2001)\nGallant, A.R., Tauchen, G.: Which moments to match? Econom. Theory 12, 657–681 (1996)\nGourieroux, C., Monfort, A., Renault, E.: Indirect inference. J. Appl. Econom. 8, S85–S118 (1993)\nHall, P., Horowitz, J., Jing, B.: On blocking rules for the bootstrap with dependent data. Biometrika 82, 561–574 (1995)\nHansen, P.R., Lunde, A.: Realized variance and market microstructure noise. J. Bus. Econ. Stat. 24, 127–218 (2006)\nHeston, S.L.: A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev. Financ. Stud. 6, 327–343 (1993)\nJacod, J., Li, Y., Mykland, P., Podolskij, M., Vetter, M.: Microstructure noise in the continuous case: the pre-averaging approach. Stoch. Process. Appl. 119, 2249–2276 (2009)\nKarlin, S., Taylor, H.M.: A First Course in Stochastic Processes. Academic Press, New York (1975)\nLahiri, S.N.: Theoretical comparison of block bootstrap methods. Ann. Stat. 27, 386–404 (1999)\nNewey, W.K., West, K.D.: A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55, 703–708 (1987)\nNolsøe, K., Nielsen, J.N., Madsen, H.: Prediction-based estimating functions for diffusion processes with measurement noise. In: Technical Reports no. 10, Informatics and mathematical modelling. Technical University of Denmark (2000)\nSørensen, M.: Prediction-based estimating functions. Econom. J. 3, 123–147 (2000)\nSørensen, M.: Prediction-based estimating functions: review and new developments. Braz. J. Probab. Stat. 25, 362–391 (2011)\nTodorov, V.: Estimation of continuous-time stochastic volatility models with jumps using high-frequency data. J. Econom. 148, 131–148 (2009)\nTodorov, V., Tauchen, G.: Simulation methods for LTvy-driven CARMA stochastic volatility models. J. Bus. Econ. 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The general equivalence theorems are given to check the optimality of a given design, based on the locally and Bayesian D-optimality criteria. The explicit characterizations of the locally and Bayesian D-optimal designs are provided. The results are illustrated by numerical analysis for a quadratic polynomial measurement error model. Numerical results show that the error-variances ratio and the model parameter are the important factors for the both optimal designs. Moreover, it is shown that the Bayesian D-optimal design is more robust and effective compared with the locally D-optimal design, if the error-variances ratio or the model parameter is misspecified.",{"EN":994},"Optimal designs for homoscedastic functional polynomial measurement error models",{"VOID":996},"[\"9698190627365937367\"]",{"VOID":998},"Atkinson, A., Donev, A., Tobias, R.: Optimum Experimental Designs, with SAS, 2nd edn. Oxford University Press, Oxford (2007)\nBuonaccorsi, J.P.: Measurement Error: Models, Methods, and Applications. Chapman and Hall, London (2010)\nCarroll, R., Ruppert, D., Stefanski, L.: Measurement Error in Nonlinear Models: A Modern Perspective, 2nd edn. Chapman and Hall, London (2006)\nCheng, C.L., Van Ness, J.W.: Statistical Regression With Measurement Error. Oxford University Press, Oxford (1999)\nChernoff, H.: Locally optimal designs for estimating parameters. Ann. Inst. Statist. Math. 24, 586–602 (1953)\nDavis, D.W., Shen, Y., Mullani, N.A., et al.: Quantitative analysis of biomarkers defines an optimal biological dose for recombinant human endostatin in primary human tumors. Clin. Cancer Res. 10, 33–42 (2004)\nDette, H., Breta, F., Pepelyshev, A., Pinheiro, J.: Optimal designs for dose-finding studies. J. Amer. Statist. Assoc. 103, 1225–1237 (2008)\nDonev, A.N.: Design of experiments in the presence of errors in factor levels. J. Stat. Plann. Inference 126, 569–585 (2004)\nDoví, V.G., Reverberi, A.P., Maga, L.: Optimal design of sequential experiments for error-in-variables models. Comput. Chem. Eng. 17, 111–115 (1993)\nFedorov, V.V.: Theory of Optimal Experiments. Academic Press, New York (1972)\nFuller, W.A.: Measurement Error Models. Wiley, New York (1987)\nFidler, I.J., Ellis, L.M.: The implications of angiogenesis for thevbiology and therapy of cancer metastasis. Cell 79, 185–188 (1994)\nGordon, T., Kannel, W.E.: Introduction and General Background in the Framingham Study- The Framinham Study, Sections 1 and 2. National Heart, Lung and Blood Institute, Betheesda, Maryland (1968)\nKarlin, S., Studden, W.J.: Tchebycheff Systems: With Applications in Analysis and Statistics. John Wiley and Sons Inc, New York (1966)\nKeeler, S., Reilly, P.: The design of experiments when there are errors in all the variables. Can. J. Chem. Eng. 70, 774–778 (1992)\nKonstantinou, M., Dette, H.: Locally optimal designs for errors-in-variables models. Biometrika 102, 951–958 (2015)\nKonstantinou, M., Dette, H.: Bayesian D-optimal designs for error-in-variables models. Appl. Stoch. Models Bus. Ind. 33, 269–281 (2017)\nKuha, J., Temple, J.: Covariate measurement error in quadratic regression. Int. Stat. Rev. 71, 131–150 (2003)\nLäuter, E.: Experimental planning in a class of models. Math. Oper. Stat. 36, 1627–1655 (1974)\nOkajima, S., Mone, M., Nakamura, T.: Mortaliity of registered A-bomb survivors in Nagasako, Japan, 1970–1984. Radiat. Res. 103, 419–431 (1985)\nO’Reilly, M.S., Boehm, T., Shing, Y., et al.: Endostatin: an endogenous inhibitor of angiogenesis and tumor growth. Cell 88, 277–285 (1997)\nPierce, D.A., Stram, D.O., Vaeth, M., Schafer, D.: Some insights into the errors in variables problem provided by consideration of radiation dose-response analysis for the A-bomb survivors. J. Amer. Stat. Assoc. 87, 351–359 (1992)\nProzato, L.: Information matrices with random regressors application to eaperimental design. J. Stat. Plann. Inference 108, 189–200 (2002)\nSilvey, S.D.: Optimal Design. Chapman and Hall, London (1980)\nWolter, K.M., Fuller, W.A.: Estimation of the quadratic errors-in-variables model. Biometrika 69, 175–182 (1982)\nZhang, M.J., Yue, R.X.: Locally D-optimal designs for heteroscedastic polynomial measurement error models. 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Rev. 12, 1–13 (1971)\nBaltagi, B.H.: An alternative heteroscedastic error component model, problem 88.2.2. Econom. Theory 4, 349–350 (1988)\nBaltagi, B.H., Griffin, J.M.: A generalized error component model with heteroscedastic disturbances. Int. Econ. Rev. 29, 745–753 (1988)\nBaltagi, B.H., Bresson, G., Pirotte, A.: Adaptive estimation of heteroskedastic error component models. Econom. Rev. 24, 39–58 (2005)\nBaltagi, B.H., Bresson, G., Pirotte, A.: Joint LM test for homoskedasticity in a one-way error component model. Econom. J. 134, 401–417 (2006)\nBasu, S., Chib, S.: Marginal likelihood and Bayes factors for Dirichlet process mixture models. J. Am. Stat. Assoc. 98, 224–235 (2003)\nBresson, G., Hsiao, C., Pirotte, A.: Assessing the contribution of R&D to total factor productivity. A Bayesian approach to account for heterogeneity and heteroscedasticity, Working paper 07-08, Ermes, Université Paris II (2007)\nBrockwell, P.J., Davis, R.A.: Time Series: Theory and Methods. Springer Series in Statistics. Springer, Berlin (1991)\nCarlin, B.P., Louis, T.A.: Bayes and Empirical Bayes Methods for Data Analysis. Chapman & Hall\u002FCRC Press, London\u002FBoca Raton (2000)\nChib, S.: Panel data modeling and inference: a Bayesian primer. In: Màtyàs, L., Sevestre, P. (eds.) The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice, pp. 479–516. Kluwer Academic, Norwell (2008)\nFriedman, M.: Essays in Positive Economics. University of Chicago Press, Chicago (1953)\nGelfand, A.E., Smith, A.F.M.: Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 46, 84–88 (1990)\nGriliches, Z.: Issues in assessing the contribution of research and development to productivity growth. Bell J. Econ. 10, 92–116 (1979)\nHall, B.H.: Industrial research during the 1980s: did the rate of return fall? Brookings papers on Economic Activity. 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