Bayesian Variable Selection for Gaussian copula regression models.

ABSTRACT: We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. A sparse Gaussian copula regression model is used to account for the multivariate dependencies between any combination of discrete and/or continuous responses and their association with a set of predictors. We utilize the parameter expansion for data augmentation strategy to construct a Markov chain Monte Carlo algorithm for the estimation of the parameters and the latent variables of the model. Based on a centered parametrization of the Gaussian latent variables, we design a fixed-dimensional proposal distribution to update jointly the latent binary vectors of important predictors and the corresponding non-zero regression coefficients. For Gaussian responses and

SUBMITTER: Alexopoulos A 

PROVIDER: S-EPMC7614421 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature