Bayesian Penalized Quantile Regression
The quantile varying coefficient model is robust to data heterogeneity, outliers and heavy-tailed distributions in the response variable due to the check loss function in quantile regression. In addition, it can flexibly model the dynamic pattern of regression coefficients through nonparametric varying coefficient functions. Although high dimensional quantile varying coefficient model has been examined extensively in the frequentist framework, the corresponding Bayesian variable selection methods have rarely been developed. In this package, we have implemented the Gibbs samplers of the penalized Bayesian quantile varying coefficient model with the spike-and-slab priors [Zhou et al.(2023)]<doi.org/10.1016/j.csda.2023.107808>. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models can be efficiently performed by using the package.
- To install from github, run these two lines of code in R
install.packages("devtools")
devtools::install_github("cenwu/pqrBayes")
This package provides implementation for methods proposed in
- Zhou, F., Ren, J., Ma, S. and Wu, C. (2023). The Bayesian Regularized Quantile Varying Coefficient Model. {\emph{Computational Statistics & Data Analysis}, 107808} \doi{10.1016/j.csda.2023.107808}