Title | Bayesian variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Ji, Y, Shi, H |
Journal | PLoS One |
Volume | 15 |
Issue | 10 |
Pagination | e0241197 |
ISSN Number | 1932-6203 |
Keywords | Bayes Theorem, Computer Simulation, Datasets as Topic, Linear Models, Longitudinal Studies, Macular Degeneration |
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. |
DOI | 10.1371/journal.pone.0241197 |
Citation Key | 12457 |
PubMed ID | 33104698 |
PubMed Central ID | PMC7588124 |