Optimal smoothing in nonparametric mixed-effect models

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dc.creatorGu, Chong
dc.creatorMa, Ping
dc.date2005-07-21
dc.date.accessioned2026-07-07T08:07:05Z
dc.date.available2026-07-07T08:07:05Z
dc.descriptionMixed-effect models are widely used for the analysis of correlated data such as longitudinal data and repeated measures. In this article, we study an approach to the nonparametric estimation of mixed-effect models. We consider models with parametric random effects and flexible fixed effects, and employ the penalized least squares method to estimate the models. The issue to be addressed is the selection of smoothing parameters through the generalized cross-validation method, which is shown to yield optimal smoothing for both real and latent random effects. Simulation studies are conducted to investigate the empirical performance of generalized cross-validation in the context. Real-data examples are presented to demonstrate the applications of the methodology.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053605000000110 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/0507428
dc.identifierhttp://arxiv.org/abs/math/0507428
dc.identifierAnnals of Statistics 2005, Vol. 33, No. 3, 1357-1379
dc.identifierdoi:10.1214/009053605000000110
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130823
dc.subjectStatistics Theory
dc.subject62G08 (Primary) 62G05, 62G20, 62H12, 41A15 (Secondary)
dc.titleOptimal smoothing in nonparametric mixed-effect models
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