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Biostatistics Advance Access published online on June 11, 2009
Biostatistics, doi:10.1093/biostatistics/kxp018
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Variable selection and dependency networks for genomewide data

Adrian Dobra*

Department of Statistics and Department of Biobehavioral Nursing and Health Systems, University of Washington Seattle, WA 98195, USA adobra{at}u.washington.edu

* To whom correspondence should be addressed.

We describe a new stochastic search algorithm for linear regression models called the bounded mode stochastic search (BMSS). We make use of BMSS to perform variable selection and classification as well as to construct sparse dependency networks. Furthermore, we show how to determine genetic networks from genomewide data that involve any combination of continuous and discrete variables. We illustrate our methodology with several real-world data sets.

Keywords: Bayesian regression analysis; Dependency networks; Gene expression; Stochastic search; Variable selection

Received September 8, 2008; revised February 4, 2009; revised April 13, 2009; accepted for publication May 13, 2009.


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