頁籤選單縮合
題 名 | Bayesian Wavelet Shrinkage for Nonparametric Mixed-Effects Models |
---|---|
作 者 | Huang,Su-yun; Lu,Henry Horng-shing; | 書刊名 | Statistica Sinica |
卷 期 | 10:4 2000.10[民89.10] |
頁 次 | 頁1021-1040 |
分類號 | 319.51 |
關鍵詞 | Bayesian regression; Besov spaces; Best linear unbiased prediction; BLUP; Gauss-Markov estimation; Generalized cross validation; Nonparametric regression; Sobolev regularization; Wavelet shrinkage; |
語 文 | 英文(English) |
英文摘要 | 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. |
本系統中英文摘要資訊取自各篇刊載內容。