Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage

Sha, Naijun and Vannucci, Marina and Tadesse, Mahlet G. and Brown, Philip J. and Dragoni, Ilaria and Davies, Nick and Roberts, Tracy C. and Contestabile, Adrea and Salmon, Mike and Buckley, Chris and Falcaiani, Francesco (2004) Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage. Biometrics, 60 (3). pp. 812-819. ISSN 0006-341X. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1111/j.0006-341X.2004.00233.x...

Abstract

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.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science
Depositing User: Philip J Brown
Date Deposited: 01 Oct 2008 14:36
Last Modified: 26 Jun 2014 15:42
Resource URI: http://kar.kent.ac.uk/id/eprint/8146 (The current URI for this page, for reference purposes)
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