BayesQRCount: Adaptive Bayesian Quantile Regression for Count Data
Implements Bayesian quantile regression for count data using the
jittering technique for discrete data smoothing and an asymmetric Laplace
distribution likelihood. Supports adaptive variable selection via a
random-bridge penalty with a beta prior on the power parameter, as well as
fixed-bridge and Lasso penalties. Utilizes Markov chain Monte Carlo with
Gibbs sampling and adaptive Metropolis-Hastings algorithms for posterior
inference, provides Gelman-Rubin convergence diagnostics, and predicts
conditional quantiles for count responses. Methodology and applications are
based on the following key references: Luo, Zhou, Hu, and Li (2026, Journal of
Mathematics, 2026:1543166, <doi:10.1155/jom/1543166>), Koenker and Bassett
(1978, Econometrica, 46, 33-50, <doi:10.2307/1913643>), Machado and Santos Silva
(2005, Journal of the American Statistical Association, 100, 1226-1237,
<doi:10.1198/016214505000000330>), Yu and Moyeed (2001, Statistics and
Probability Letters, 54, 437-447, <doi:10.1016/S0167-7152(01)00124-9>), Polson,
Scott, and Windle (2014, Journal of the Royal Statistical Society Series B, 76,
713-733, <doi:10.1111/rssb.12042>), Park and Casella (2008, Journal of the
American Statistical Association, 103, 681-686, <doi:10.1198/016214508000000337>),
and Roberts and Rosenthal (2009, Journal of Computational and Graphical
Statistics, 18, 349-367, <doi:10.1198/jcgs.2009.06134>).
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=BayesQRCount
to link to this page.
[aut, cre],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut]