Bayesian Quantile Regression for Ordinal Longitudinal Data

TL;DR

This paper introduces a novel Bayesian ordinal quantile regression model with random effects for longitudinal data, providing an efficient Gibbs sampling algorithm, and demonstrating its effectiveness through simulations and real data analysis.

Contribution

It is the first to develop quantile regression methods specifically for longitudinal data with ordinal outcomes, advancing statistical modeling capabilities.

Findings

Effective Gibbs sampling algorithm for the model.

Successful application to simulated and real datasets.

First approach to ordinal quantile regression for longitudinal data.

Abstract

Since the pioneering work by Koenker and Bassett (1978), quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location scale mixture representation of the skewed double exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.