Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates.

ABSTRACT: We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectation-maximization variable selection (EMVS) with a deterministic annealing variant as the platform for our method, due to its proven flexibility and efficiency. We propose a variance adjustment of the priors for the coefficients of qualitative covariates, which controls false-positive rates, and a flexible parameterization for interaction terms, which accommodates user-specified heredity constraints. This method can handle all pairwise interaction terms as well as a subset of specific interactions. Using simulation, we show that this method selects associated covariates better than t

SUBMITTER: Koslovsky MD 

PROVIDER: S-EPMC5935273 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature