  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.
