Optimal False Discovery Rate Control with Kernel Density Estimation in a Microarray Experiment

Most of current false discovery rate (FDR) procedures in a microarray experiment assume restrictive dependence structures, resulting in being less reliable. FDR controlling procedure under suitable dependence structures based on Poisson distributional approximation is shown. Unlike other procedures,...

Full description

Saved in:
Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 45; no. 3; pp. 771 - 780
Main Author Kang, Moonsu
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 15.03.2016
Taylor & Francis Ltd
Subjects
Approximation
Automobile insurance
Computation
Computer simulation
Density
Experiments
False discovery rate
False nondiscovery rate
Genes
Insurance claims
Kernel density estimation
Kernels
Mathematical analysis
Microarray data
Optimization
Poisson approximation
Primary 62
Secondary H15
Online AccessGet full text

Cover

More Information
Summary:Most of current false discovery rate (FDR) procedures in a microarray experiment assume restrictive dependence structures, resulting in being less reliable. FDR controlling procedure under suitable dependence structures based on Poisson distributional approximation is shown. Unlike other procedures, the distribution of false null hypotheses is estimated by using kernel density estimation allowing for dependent structures among the genes. Furthermore, we develop an FDR framework that minimizes the false nondiscovery rate (FNR) with a constraint on the controlled level of the FDR. The performance of the proposed FDR procedure is compared with that of other existing FDR controlling procedures, with an application to the microarray study of simulated data.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2013.875569