TY - JOUR T1 - Bayesian variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration. JF - PLoS One Y1 - 2020 A1 - Ji, Yonggang A1 - Shi, Haifang KW - Bayes Theorem KW - Computer Simulation KW - Datasets as Topic KW - Linear Models KW - Longitudinal Studies KW - Macular Degeneration AB -

This paper presents a Bayesian analysis of linear mixed models for quantile regression based on a Cholesky decomposition for the covariance matrix of random effects. We develop a Bayesian shrinkage approach to quantile mixed regression models using a Bayesian adaptive lasso and an extended Bayesian adaptive group lasso. We also consider variable selection procedures for both fixed and random effects in a linear quantile mixed model via the Bayesian adaptive lasso and extended Bayesian adaptive group lasso with spike and slab priors. To improve mixing of the Markov chains, a simple and efficient partially collapsed Gibbs sampling algorithm is developed for posterior inference. Simulation experiments and an application to the Age-Related Macular Degeneration Trial data to demonstrate the proposed methods.

VL - 15 IS - 10 ER -