標題: Bayesian wavelet shrinkage for nonparametric mixed-effects models
作者: Huang, SY
Lu, HHS
統計學研究所
Institute of Statistics
關鍵字: Bayesian regression;Besov spaces;best linear unbiased prediction (BLUP);Gauss-Markov estimation;generalized cross validation;non-parametric regression;Sobolev regularization;wavelet shrinkage
公開日期: 1-Oct-2000
摘要: The main purpose of this article is to study the wavelet shrinkage method from a Bayesian viewpoint. Nonparametric mixed-effects models are proposed and used for interpretation of the Bayesian structure. Bayes and empirical Bayes estimation are discussed. The latter is shown to have the Gauss-Markov type optimality (i.e., BLUP), to be equivalent to a method of regularization estimator (MORE), and to be minimax in a certain class. Characterization of prior and posterior regularity is discussed. The smoothness of posterior estimators is controlled via prior parameters. Computational issues including the use of generalized cross validation are discussed, and examples are presented.
URI: http://hdl.handle.net/11536/30229
ISSN: 1017-0405
期刊: STATISTICA SINICA
Volume: 10
Issue: 4
起始頁: 1021
結束頁: 1040
Appears in Collections:Articles