A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models.

ABSTRACT: A new empirical Bayes approach to variable selection in the context of generalized linear models is developed. The proposed algorithm scales to situations in which the number of putative explanatory variables is very large, possibly much larger than the number of responses. The coefficients in the linear predictor are modeled as a three-component mixture allowing the explanatory variables to have a random positive effect on the response, a random negative effect, or no effect. A key assumption is that only a small (but unknown) fraction of the candidate variables have a non-zero effect. This assumption, in addition to treating the coefficients as random effects facilitates an approach that is computationally efficient. In particular, the number of parameters that have to be estimated is sm

SUBMITTER: Bar HY 

PROVIDER: S-EPMC11198964 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature