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dc.contributor.authorLv, Jinchi
dc.contributor.authorLiu, Jun
dc.date.accessioned2017-08-07T19:16:54Z
dc.date.issued2013
dc.identifier.citationLv, Jinchi, and Jun S. Liu. 2013. “Model Selection Principles in Misspecified Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (1) (July 3): 141–167. doi:10.1111/rssb.12023.en_US
dc.identifier.issn1369-7412en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:33719950
dc.description.abstractModel selection is of fundamental importance to high dimensional modelling featured in many contemporary applications. Classical principles of model selection include the Bayesian principle and the Kullback–Leibler divergence principle, which lead to the Bayesian information criterion and Akaike information criterion respectively, when models are correctly specified. Yet model misspecification is unavoidable in practice. We derive novel asymptotic expansions of the two well-known principles in misspecified generalized linear models, which give the generalized Bayesian information criterion and generalized Akaike information criterion. A specific form of prior probabilities motivated by the Kullback–Leibler divergence principle leads to the generalized Bayesian information criterion with prior probability, inline image, which can be naturally decomposed as the sum of the negative maximum quasi-log-likelihood, a penalty on model dimensionality, and a penalty on model misspecification directly. Numerical studies demonstrate the advantage of the new methods for model selection in both correctly specified and misspecified models.en_US
dc.description.sponsorshipStatisticsen_US
dc.language.isoen_USen_US
dc.publisherWiley-Blackwellen_US
dc.relation.isversionofdoi:10.1111/rssb.12023en_US
dash.licenseMETA_ONLY
dc.subjectAkaike information criterionen_US
dc.subjectBayesian information criterionen_US
dc.subjectBayesian principleen_US
dc.subjectGeneralized Akaike information criterionen_US
dc.subjectGeneralized Bayesian information criterionen_US
dc.subjectGeneralized Bayesian information criterion with prior probabilityen_US
dc.subjectKullback–Leibler divergence principleen_US
dc.subjectModel misspecificationen_US
dc.subjectModel selectionen_US
dc.titleModel selection principles in misspecified modelsen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalJournal of the Royal Statistical Society: Series B (Statistical Methodology)en_US
dash.depositing.authorLiu, Jun
dash.embargo.until10000-01-01
dc.identifier.doi10.1111/rssb.12023*
workflow.legacycommentsLiu emailed 2016-05-04 AD Liu emailed 2017-02-23 MM meta.darken_US
dash.contributor.affiliatedLiu, Jun


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