Bayesian joint quantile regression for mixed effects models with censoring and errors in covariates

Yuzhu Tian (), Er’qian Li and Maozai Tian
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Yuzhu Tian: Henan University of Science and Technology
Er’qian Li: Renmin University of China
Maozai Tian: Renmin University of China

Computational Statistics, 2016, vol. 31, issue 3, No 11, 1057 pages

Abstract: Abstract In this paper, we discuss Bayesian joint quantile regression of mixed effects models with censored responses and errors in covariates simultaneously using Markov Chain Monte Carlo method. Under the assumption of asymmetric Laplace error distribution, we establish a Bayesian hierarchical model and derive the posterior distributions of all unknown parameters based on Gibbs sampling algorithm. Three cases including multivariate normal distribution and other two heavy-tailed distributions are considered for fitting random effects of the mixed effects models. Finally, some Monte Carlo simulations are performed and the proposed procedure is illustrated by analyzing a group of AIDS clinical data set.

Keywords: Longitudinal data; Censoring; Errors in covariates; Bayesian quantile regression; Heavy-tailed random effects (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s00180-016-0659-1

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