Bayesian variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration

Yonggang Ji and Haifang Shi

PLOS ONE, 2020, vol. 15, issue 10, 1-34

Abstract: 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0241197

DOI: 10.1371/journal.pone.0241197

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