pqrBayes: Bayesian Penalized Quantile Regression
The quantile varying coefficient model is robust to data heterogeneity,
outliers and heavy-tailed distributions in the response variable. In addition,
it can flexibly model dynamic patterns of regression coefficients through
nonparametric varying coefficient functions. In this package, we have implemented
the Gibbs samplers of the penalized Bayesian quantile varying coefficient model with
spike-and-slab priors [Zhou et al.(2023)]<doi:10.1016/j.csda.2023.107808> for efficient
Bayesian shrinkage estimation, variable selection and statistical inference. In particular,
valid Bayesian inferences on sparse quantile varying coefficient functions can be validated
on finite samples. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed
and alternative models can be efficiently performed by using the package.
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