  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.
