Bayesian Variable Selection for Gaussian copula regression models

07/18/2019
by   Angelos Alexopoulos, et al.
0

We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. In particular, a sparse Gaussian copula regression model is used to account for the multivariate dependencies between any combination of discrete and 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 an efficient proposal distribution to update jointly the latent binary vectors of important predictors and the corresponding non-zero regression coefficients. The proposed strategy is tested on simulated data and applied to two real data sets in which the responses consist of low-intensity counts, binary, ordinal and continuous variables.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/09/2016

Sparse additive Gaussian process with soft interactions

Additive nonparametric regression models provide an attractive tool for ...
research
01/04/2023

Censored Regression with Serially Correlated Errors: a Bayesian approach

The problem of estimating censored linear regression models with autocor...
research
04/23/2021

Latent variable models for multivariate dyadic data with zero inflation: Analysis of intergenerational exchanges of family support

Understanding the help and support that is exchanged between family memb...
research
07/13/2018

Sequential sampling of Gaussian latent variable models

We consider the problem of inferring a latent function in a probabilisti...
research
05/17/2022

Bayesian Discrete Conditional Transformation Models

We propose a novel Bayesian model framework for discrete ordinal and cou...
research
01/04/2011

Sparse Partitioning: Nonlinear regression with binary or tertiary predictors, with application to association studies

This paper presents Sparse Partitioning, a Bayesian method for identifyi...
research
03/12/2023

Bayesian Size-and-Shape regression modelling

Building on Dryden et al. (2021), this note presents the Bayesian estima...

Please sign up or login with your details

Forgot password? Click here to reset