Tail-adaptive Bayesian shrinkage

arXiv:2007.02192v4 Announce Type: replace-cross
Abstract: Robust Bayesian methods for high-dimensional regression problems under diverse sparse regimes are studied. Traditional shrinkage priors are primarily designed to detect a handful of signals from tens of thousands of predictors in the so-called ultra-sparsity domain. However, they may not perform desirably when the degree of sparsity is moderate. In this paper, we propose a robust sparse estimation method under diverse sparsity regimes, which has a tail-adaptive shrinkage property. In this property, the tail-heaviness of …

abstract arxiv bayesian diverse domain math.st paper regression robust shrinkage sparsity stat.ap stat.co stat.me stat.ml stat.th type