文章基本信息

标题：Optimal Smoothing with Correlated Data
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作者：Chun Han University of Kansas ; USA Chong Gu Purdue University, USA
期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2008
卷号：70
期号：01
页码：38--72
出版社：Indian Statistical Institute
摘要：Penalized likelihood method offers versatile smoothing techniques in a vari- ety of stochastic settings, and the proper selection of the smoothing param- eters and other tuning parameters is crucial to the practical performance of penalized likelihood estimates. In this article, we study the selection of the smoothing parameters and the correlation parameters in penalized like- lihood regression with correlated data. We propose a simple modification of Mallows¡¯ CL to accommodate the correlation parameters, and derive a profiled version for use with unknown variance. The proposed methods are shown to be optimal in a certain sense through asymptotic analysis and nu- merical simulations. Real-data example is also presented and related issues discussed.
关键词：Correlated data, cross-validation, penalized likeli- hood, regression.