Nonlinear wavelet shrinkage with Bayes rules and Bayes factors

被引：170

作者：

Vidakovic, B ^{[1
]}

机构：

[1] Duke Univ, Inst Stat & Decis Sci, Durham, NC 27708 USA

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 1998年 / 93卷 / 441期

关键词：

Bayes model; denoising; thresholding; wavelet regression;

D O I：

10.2307/2669614

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：

Wavelet shrinkage, the method proposed by the seminal work of Donoho and Johnstone is a disarmingly simple and efficient way of denoising data. Shrinking wavelet coefficients was proposed from several optimality criteria. In this article a wavelet shrinkage by coherent Bayesian inference in the wavelet domain is proposed. The methods are tested on standard Donoho-Johnstone test functions.

引用

页码：173 / 179

页数：7

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