Bayesian Quantile Regression for Longitudinal Count Data

TL;DR

This paper develops a Bayesian quantile regression framework tailored for longitudinal count data, incorporating smoothing and efficient Gibbs sampling, with validation through simulations and a neurology application.

Contribution

It introduces a novel Bayesian quantile regression model for count data with longitudinal structure, using asymmetric Laplace distribution and Gibbs sampling for inference.

Findings

Model performs well in simulations

Application to neurology data demonstrates practical utility

Outperforms traditional methods in certain scenarios

Abstract

This work introduces Bayesian quantile regression modeling framework for the analysis of longitudinal count data. In this model, the response variable is not continuous and hence an artificial smoothing of counts is incorporated. The Bayesian implementation utilizes the normal-exponential mixture representation of the asymmetric Laplace distribution for the response variable. An efficient Gibbs sampling algorithm is derived for fitting the model to the data. The model is illustrated through simulation studies and implemented in an application drawn from neurology. Model comparison demonstrates the practical utility of the proposed model.