A semi-parametric approach for mixture models: Application to local false discovery rate estimation

被引：37
|
作者：
Robin, Stephane
Bar-Hen, Avner
Daudin, Jean-Jacques
Pierre, Laurent
机构：
[1] INRA, UMR AgroParisTech 518, F-75005 Paris, France
[2] Univ Paris 10, F-92001 Nanterre, France
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2007年 / 51卷 / 12期
关键词：
false discovery rate; mixture model; multiple testing procedure; Semi-parametric density estimation;
D O I：
10.1016/j.csda.2007.02.028
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A procedure to estimate a two-component mixture model where one component is known is proposed. The unknown part is estimated with a weighted kernel function. The weights are defined in an adaptive way. The convergence to a unique solution of our estimation procedure is proven. The procedure is compared with two classical approaches using simulation. In addition, the results obtained are applied to multiple testing procedure in order to estimate the posterior population probabilities and the local false discovery rate. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码：5483 / 5493
页数：11
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