Bayesian Methodology for Adaptive Sparsity and Shrinkage in Regression
回归中自适应稀疏性与收缩的贝叶斯方法
Bayesian and frequentist regularization techniques are commonly employed for estimating regression models from data. However, most existing methods require practitioners to pre-specify whether the target model is dense or sparse. A recent proposal, the GLP prior distribution, addresses this limitation by bridging the gap between sparse and dense model regimes in a data-driven manner. Despite its flexibility, the GLP prior incurs high computational costs due to matrix…