Asymptotic optimality of a cross-validatory predictive approach to linear model selection

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dc.creatorChakrabarti, Arijit
dc.creatorSamanta, Tapas
dc.date2008-05-21
dc.date.accessioned2026-07-07T12:19:08Z
dc.date.available2026-07-07T12:19:08Z
dc.descriptionIn this article we study the asymptotic predictive optimality of a model selection criterion based on the cross-validatory predictive density, already available in the literature. For a dependent variable and associated explanatory variables, we consider a class of linear models as approximations to the true regression function. One selects a model among these using the criterion under study and predicts a future replicate of the dependent variable by an optimal predictor under the chosen model. We show that for squared error prediction loss, this scheme of prediction performs asymptotically as well as an oracle, where the oracle here refers to a model selection rule which minimizes this loss if the true regression were known.
dc.descriptionPublished in at http://dx.doi.org/10.1214/074921708000000110 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0805.3238
dc.identifierhttp://arxiv.org/abs/0805.3238
dc.identifierIMS Collections 2008, Vol. 3, 138-154
dc.identifierdoi:10.1214/074921708000000110
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212647
dc.subjectStatistics Theory
dc.subjectMethodology
dc.subject62J05 (Primary) 62F15 (Secondary)
dc.titleAsymptotic optimality of a cross-validatory predictive approach to linear model selection
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