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Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.

Original publication

DOI

10.1111/j.0006-341X.2004.00233.x

Type

Journal article

Journal

Biometrics

Publication Date

09/2004

Volume

60

Pages

812 - 819

Keywords

Arthritis, Rheumatoid, Bayes Theorem, Biometry, Humans, Markov Chains, Models, Biological, Models, Statistical, Monte Carlo Method, Oligonucleotide Array Sequence Analysis