Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction* 论文

详细信息

发表期刊/会议

Oxford University Press eBooks

发表日期

2011-10-06

发表年份

2011

摘要

We use Lévy processes to generate joint prior distributions for a location parameter β = (β1,..., βp) as p grows large. This approach, which generalizes normal scale-mixture priors to an infinite-dimensional setting, has a number of connections with mathematical finance and Bayesian nonparametrics. We argue that it provides an intuitive framework for generating new regularization penalties and shrinkage rules; for performing asymptotic analysis on existing models; and for simplifying proofs of some classic results on normal scale mixtures.