期刊
STATISTICAL SCIENCE
卷 34, 期 3, 页码 405-427出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/19-STS700
关键词
Global-local priors; horseshoe; horseshoe; hyper-parameter tuning; Lasso; regression; regularization; sparsity
资金
- NSF [DMS-1613063]
The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. Lasso is a gold standard for predictor selection while horseshoe is a state-of-the-art Bayesian estimator for sparse signals. Lasso is fast and scalable and uses convex optimization whilst the horseshoe is nonconvex. Our novel perspective focuses on three aspects: (i) theoretical optimality in high-dimensional inference for the Gaussian sparse model and beyond, (ii) efficiency and scalability of computation and (iii) methodological development and performance.
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