Bayesian block-diagonal variable selection and model averaging.

ABSTRACT: We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also provides a novel and efficient solution to the frequentist best subset selection problem for block-diagonal designs. Posterior probabilities for any number of models are obtained by evaluating a single one-dimensional integral, and other quantities of interest such as variable inclusion probabilities and model-averaged regression estimates are obtained by an adaptive, deterministic one-dimension

SUBMITTER: Papaspiliopoulos O 

PROVIDER: S-EPMC5975653 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature