Bayesian Lasso-mixed quantile regression
In this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l
1 penalty in the mixed quantile regression check function.
A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of an age-related macular degeneration (ARMD) data, we assess the performance of the proposed method. The simulation studies and the ARMD data analysis indicate
that the proposed method performs well in comparison with the other approaches.
Keywords: Gibbs sampler; asymmetric Laplace distribution; longitudinal data; quantile regression; random effects
Document Type: Research Article
Affiliations: Department of Mathematical Sciences, Brunel University, Uxbridge, UB8 3PH, UK
Publication date: 03 April 2014
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