Paper Info
Title
Estimating the Proportion of True Null Hypotheses in Nonparametric Exponential Mixture Model with Appication to the Leukemia Gene Expression Data.
Abstract
We revisit the problem of estimating the proportion pi of true null hypotheses where a large scale of parallel hypothesis tests are performed independently. While the proportion is a quantity of interest in its own right in applications, the problem has arisen in assessing or controlling an overall false discovery rate. On the basis of a Bayes interpretation of the problem, the marginal distribution of the p-value is modeled in a mixture of the uniform distribution (null) and a non-uniform distribution (alternative), so that the parameter pi of interest is characterized as the mixing proportion of the uniform component on the mixture. In this article, a nonparametric exponential mixture model is proposed to fit the p-values. As an alternative approach to the convex decreasing mixture model, the exponential mixture model has the advantages of identifiability, flexibility, and regularity. A computation algorithm is developed. The new approach is applied to a leukemia gene expression data set where multiple significance tests over 3,051 genes are performed. The new estimate for pi with the leukemia gene expression data appears to be about 10% lower than the other three estimates that are known to be conservative. Simulation results also show that the new estimate is usually lower and has smaller bias than the other three estimates.
Year
DOI
Venue
2012
10.1080/03610918.2011.611308
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Keywords
DocType
Volume
Aitken's acceleration rule,CNM algorithm,EM algorithm,FDR,Multiple testing,Nonparametric mixture model
Journal
41
Issue
ISSN
Citations 
9
0361-0918
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Hualing Zhao1161.72
Xiaoxia Wu253538.61
Hong Zhang314922.43
Hanfeng Chen4175.51