From finite sample to asymptotics: A geometric bridge for selection criteria in spline regression

dc.creatorKou, S. C.
dc.date2005-08-30
dc.date.accessioned2026-07-07T08:07:15Z
dc.date.available2026-07-07T08:07:15Z
dc.descriptionThis paper studies, under the setting of spline regression, the connection between finite-sample properties of selection criteria and their asymptotic counterparts, focusing on bridging the gap between the two. We introduce a bias-variance decomposition of the prediction error, using which it is shown that in the asymptotics the bias term dominates the variability term, providing an explanation of the gap. A geometric exposition is provided for intuitive understanding. The theoretical and geometric results are illustrated through a numerical example.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053604000000841 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0508596
dc.identifierhttp://arxiv.org/abs/math/0508596
dc.identifierAnnals of Statistics 2004, Vol. 32, No. 6, 2444-2468
dc.identifierdoi:10.1214/009053604000000841
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130877
dc.subjectStatistics Theory
dc.subject62G08 (Primary) 62G20. (Secondary)
dc.titleFrom finite sample to asymptotics: A geometric bridge for selection criteria in spline regression
dc.typetext

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