Bayenet - Bayesian Quantile Elastic Net for Genetic Study
As heavy-tailed error distribution and outliers in the
response variable widely exist, models which are robust to data
contamination are highly demanded. Here, we develop a novel
robust Bayesian variable selection method with elastic net
penalty for quantile regression in genetic analysis. In
particular, the spike-and-slab priors have been incorporated to
impose sparsity. An efficient Gibbs sampler has been developed
to facilitate computation.The core modules of the package have
been developed in 'C++' and R.
