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June 2005 Optimal smoothing in nonparametric mixed-effect models
Chong Gu, Ping Ma
Ann. Statist. 33(3): 1357-1379 (June 2005). DOI: 10.1214/009053605000000110


Mixed-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.


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Chong Gu. Ping Ma. "Optimal smoothing in nonparametric mixed-effect models." Ann. Statist. 33 (3) 1357 - 1379, June 2005.


Published: June 2005
First available in Project Euclid: 1 July 2005

zbMATH: 1072.62027
MathSciNet: MR2195638
Digital Object Identifier: 10.1214/009053605000000110

Primary: 62G08
Secondary: 41A15 , 62G05 , 62G20 , 62H12

Keywords: Correlated error , generalized cross-validation , longitudinal data , mixed-effect model , penalized least squares , repeated measures , smoothing spline

Rights: Copyright © 2005 Institute of Mathematical Statistics


Vol.33 • No. 3 • June 2005
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