The Bayesian Reciprocal Bridge for Composite Quantile Regression with Ordinal Longitudinal Data

Abstract

This paper proposes, a Bayesian reciprocal bridge composite quantile regression is proposed for variable selection and estimation in ordinal Longitudinal  data. A new Gibbs sampling algorithm is constructed for sampling from the full conditional posterior distributions. The proposed approach is illustrated using simulation studies.  By using the simulation studies example, we show that the performance of the proposed approach is very well compared with the existing approaches.