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Biostatistics Advance Access originally published online on April 7, 2006
Biostatistics 2007 8(1):86-100; doi:10.1093/biostatistics/kxj035
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Regularized linear discriminant analysis and its application in microarrays

Yaqian Guo*

Department of Statistics, Stanford University, Stanford, CA 94305, USA yaqiang{at}stanford.edu

Trevor Hastie

Department of Statistics, Stanford University, Stanford, CA 94305, USA

Robert Tibshirani

Department of Health Research and Policy, Redwood Building, Room T101C, Stanford University, Stanford, CA 94305, USA

* To whom correspondence should be addressed.

In this paper, we introduce a modified version of linear discriminant analysis, called the "shrunken centroids regularized discriminant analysis" (SCRDA). This method generalizes the idea of the "nearest shrunken centroids" (NSC) (Tibshirani and others, 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named "rda") is available on CRAN (http://www.r-project.org) for download and testing.

Keywords: Classification; Discriminant analysis; Microarray; Prediction analysis of microarrays (PAM); Regularization; Shrunken centriods


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