Bayesian quantile regression for ordinal longitudinal data

ABSTRACT

Since the pioneering work by Koenker and Bassett [27], 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.

KEYWORDS:

• Bayesian inference
• cut-points
• longitudinal data
• ordinal regression
• quantile regression

Disclosure statement

No potential conflict of interest was reported by the authors.