Robust high-dimensional Bayesian regression with non-Gaussian errors under global--local shrinkage priors
非高斯误差下全局-局部收缩先验的稳健高维贝叶斯回归
Multivariate regression with many correlated responses and predictors commonly violates Gaussian error assumptions due to heavy tails, outliers, and asymmetry. Gaussian procedures then lose efficiency in coefficient estimation and produce biased estimates of conditional dependence graphs. We develop a robust Bayesian framework using a scale-location mixture error distribution and horseshoe+ global-local priors on both the regression coefficients and off-diagonals of the…