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Unpublished Paper
A Bayesian method for finding interactions in genomic studies
The University of Michigan Department of Biostatistics Working Paper Series
  • Wei Chen, University of Michigan Biostatistics
  • Debashis Ghosh, University of Michigan
  • Trivellore E. Raghuanthan, University of Michigan
  • Sharon Kardia, University of Michigan
Date of this Version
An important step in building a multiple regression model is the selection of predictors. In genomic and epidemiologic studies, datasets with a small sample size and a large number of predictors are common. In such settings, most standard methods for identifying a good subset of predictors are unstable. Furthermore, there is an increasing emphasis towards identification of interactions, which has not been studied much in the statistical literature. We propose a method, called BSI (Bayesian Selection of Interactions), for selecting predictors in a regression setting when the number of predictors is considerably larger than the sample size with a focus towards selecting interactions. Latent variables are used to infer subset choices based on the posterior distribution. Inference about interactions is implemented by a constraint on the latent variables. The posterior distribution is computed using the Gibbs Sampling methods. The finite-sample properties of the proposed method are assessed by simulation studies. We illustrate the BSI method by analyzing data from a hypertension study involving Single Nucleotide Polymorphisms (SNPs).
Citation Information
Wei Chen, Debashis Ghosh, Trivellore E. Raghuanthan and Sharon Kardia. "A Bayesian method for finding interactions in genomic studies" (2004)
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