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Multiple testing procedures under confounding

Debashis Ghosh

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Abstract

While multiple testing procedures have been the focus of much statistical research, an important facet of the problem is how to deal with possible confounding. Procedures have been developed by authors in genetics and statistics. In this chapter, we relate these proposals. We propose two new multiple testing approaches within this framework. The first combines sensitivity analysis methods with false discovery rate estimation procedures. The second involves construction of shrinkage estimators that utilize the mixture model for multiple testing. The procedures are illustrated with applications to a gene expression profiling experiment in prostate cancer.

Chapter information

Source
N. Balakrishnan, Edsel A. Peña and Mervyn J. Silvapulle, eds., Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008), 243-256

Dates
First available in Project Euclid: 1 April 2008

Permanent link to this document
https://projecteuclid.org/euclid.imsc/1207058277

Digital Object Identifier
doi:10.1214/193940307000000176

Mathematical Reviews number (MathSciNet)
MR2462209

Subjects
Primary: 62P10: Applications to biology and medical sciences
Secondary: 92D10: Genetics {For genetic algebras, see 17D92}

Keywords
association studies empirical null hypothesis multiple comparisons statistical genomics

Rights
Copyright © 2008, Institute of Mathematical Statistics

Citation

Ghosh, Debashis. Multiple testing procedures under confounding. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen, 243--256, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008. doi:10.1214/193940307000000176. https://projecteuclid.org/euclid.imsc/1207058277


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