Full Bayesian wavelet inference with a nonparametric prior

被引:1
|
作者
Wang, Xue [1 ]
Walker, Stephen G. [1 ]
机构
[1] Univ Kent Canterbury, Canterbury, Kent, England
关键词
Stick-breaking priors; Slice sampling; Wavelet shrinkage; Consistency; MULTIPLE SHRINKAGE; BLOCK SHRINKAGE; SELECTION; ESTIMATORS; REGRESSION; OPTIMALITY; MODEL;
D O I
10.1016/j.jspi.2012.05.010
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In this paper, we introduce a new Bayesian nonparametric model for estimating an unknown function in the presence of Gaussian noise. The proposed model involves a mixture of a point mass and an arbitrary (nonparametric) symmetric and unimodal distribution for modeling wavelet coefficients. Posterior simulation uses slice sampling ideas and the consistency under the proposed model is discussed. In particular, the method is shown to be computationally competitive with some of best Empirical wavelet estimation methods. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:55 / 62
页数:8
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