An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models

Bayesian variable selection is an important method for discovering variables which are most useful for explaining the variation in a response. The widespread use of this method has been restricted by the challenging computational problem of sampling from the corresponding posterior distribution. Recently, the use of adaptive Monte Carlo methods has been shown to lead to performance improvement over traditionally used algorithms in linear regression models. This paper looks at applying one of these algorithms (the adaptively scaled independence sampler) to logistic regression and accelerated failure time models. We investigate the use of this algorithm with data augmentation, Laplace approximation and the correlated pseudo-marginal method. The performance of the algorithms is compared on several genomic data sets.