print.pqrBayes

Bayesian regularized quantile regression utilizing sparse priors to
impose exact sparsity leads to efficient Bayesian shrinkage estimation, variable
selection and statistical inference. In this package, we have implemented robust
Bayesian variable selection with spike-and-slab priors under high-dimensional
linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and
Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying
coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular,
valid robust Bayesian inferences under both models in the presence of heavy-tailed errors
can be validated on finite samples. Additional models including robust Bayesian
group LASSO are also included. The Markov Chain Monte Carlo (MCMC) algorithms
of the proposed and alternative models are implemented in C++.

Cen Wu

pqrBayes

Bayesian Penalized Quantile Regression

Kun Fan

Jie Ren

Xiaoxi Li

Fei Zhou

print.pqrBayes function

<dl><dt>x</dt>
<dd>pqrBayes result.</dd>
<dt>digits</dt>
<dd>significant digits in printout.</dd>
<dt>...</dt>
<dd>other print arguments.</dd></dl>

Arguments

print a pqrBayes result — print.pqrBayes

<dl>

<dt>x</dt>
<dd>pqrBayes result.</dd>


<dt>digits</dt>
<dd>significant digits in printout.</dd>


<dt>...</dt>
<dd>other print arguments.</dd>

</dl>

print a pqrBayes result

print.pqrBayes: print a pqrBayes result

Description

Usage

Value

Arguments

See Also