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

@article{Sha2004BayesianVS,
  title={Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.},
  author={Naijun Sha and Marina Vannucci and Mahlet G. Tadesse and Philip JB Brown and Ilaria Dragoni and Nick Cheshire Meryl Davies and Tracy C Roberts and Andrea Contestabile and Michael A. Salmon and Chris Buckley and Francesco Falciani},
  journal={Biometrics},
  year={2004},
  volume={60 3},
  pages={
          812-9
        }
}
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… CONTINUE READING
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