Statistical Modelling 19 (6) (2019), 653–673
A Bayesian two-part quantile regression model for count data with excess zeros
Clay King,
Department of Statistical Science,
Baylor University,
Waco, TX,
USA.
and
Department of Computer Science, Mathematics and Statistics,
Colorado Mesa University,
Grand Junction, CO,
USA.
Joon Jin Song,
Department of Computer Science, Mathematics and Statistics,
Colorado Mesa University,
Grand Junction, CO,
USA.
e-mail: Joon_Song@baylor.edu
Abstract:
Quantile regression (QR) allows one to model the effect of covariates across the entire response distribution, rather than only at the mean, but QR methods have been almost exclusively applied to continuous response variables produced by a single data-generating process. Of the few studies that have performed QR on count data, none have accounted for excess zeros from a Bayesian perspective, as does the hurdle model that we propose. In this article, we propose a Bayesian two-part QR model for count data with excess zeros. The proposed model is compared to a frequentist approach via simulation, and its usefulness is displayed on two real datasets. In each application, multiple covariates are found to have differing effects across the response distribution, with special attention given to the nature of those effects in the outermost response distribution quantiles.
Keywords:
Bayesian methods; count data; excess zeros; hurdle model; quantile regression.
Downloads:
Example code in
zipped archive. The data sets used throughout this paper are available through certain R packages.
back