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arXiv · 1706.10179

Lasso Meets Horseshoe : A Survey

Anindya Bhadra·Jyotishka Datta·Nicholas G. Polson·Brandon T. Willard
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Abstract

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 non-convex. 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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Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, Brandon T. Willard. 2019-03-03. Lasso Meets Horseshoe : A Survey. https://arxiv.org/abs/1706.10179

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