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• W1543412944 abstract "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). A Bayesian method for finding interactions in genomic studies Wei Chen1∗ Debashis Ghosh1 Trivellore Raghunathan1 Sharon Kardia2 1 Department of Biostatistics 2 Department of Epidemiology University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029 ∗To Whom correspondence should be addressed Contact: lisachen@umich.edu Tel:(734) 647-3721 Fax:(734) 996-0193 Hosted by The Berkeley Electronic Press A Bayesian method for finding interactions in genomic studies" @default.
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• W1543412944 date "2004-01-01" @default.
• W1543412944 modified "2023-09-26" @default.
• W1543412944 title "A Bayesian method for finding interactions in genomic studies" @default.
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