Bayesian Quantile Regression with Adaptive Elastic Net Penalty for Longitudinal Data

Longitudinal studies include the important parts of epidemiological surveys, clinical trials and social studies. In longitudinal studies, measurement of the responses is conducted repeatedly through time. Often, the main goal is to characterize the change in responses over time and the factors that influence the change. Recently, to analyze this kind of data, quantile regression has been taken into consideration. In this paper, quantile regression model, by adding an adaptive elastic net penalty term to the random effects, is proposed and analyzed from a Bayesian point of view. Since, in this model posterior distribution of the parameters are not in explicit form, the full conditional posterior distributions of the parameters are calculated and the Gibbs sampling algorithm is used for deduction. To compare the performance of the proposed method with the conventional methods, a simulation study was conducted and at the end, applications to a real data set are illustrated