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Correcting for selection bias via cross-validation in the classification of microarray data

J. Chevelu, G. J. McLachlan, and J. Zhu

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There is increasing interest in the use of diagnostic rules based on microarray data. These rules are formed by considering the expression levels of thousands of genes in tissue samples taken on patients of known classification with respect to a number of classes, representing, say, disease status or treatment strategy. As the final versions of these rules are usually based on a small subset of the available genes, there is a selection bias that has to be corrected for in the estimation of the associated error rates. We consider the problem using cross-validation. In particular, we present explicit formulae that are useful in explaining the layers of validation that have to be performed in order to avoid improperly cross-validated estimates.

Chapter information

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), 364-376

First available in Project Euclid: 1 April 2008

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Digital Object Identifier

Primary: 62H30: Classification and discrimination; cluster analysis [See also 68T10, 91C20]
Secondary: 62H12: Estimation

cross-validation discriminant analysis error rate estimation gene expression data selection bias

Copyright © 2008, Institute of Mathematical Statistics


McLachlan, G. J.; Chevelu, J.; Zhu, J. Correcting for selection bias via cross-validation in the classification of microarray data. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen, 364--376, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008. doi:10.1214/193940307000000284.

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