# Signal Adaptive Variable Selector for the Horseshoe Prior

arXiv: https://arxiv.org/abs/1810.09004 · HTML: https://mlsanity.com/paper/1810.09004/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Decoupling Shrinkage and Selection for the Bayesian Quantile Regression](https://mlsanity.com/paper/2107.08498/) (2021)

## Abstract (arXiv)

In this article, we propose a simple method to perform variable selection as a post model-fitting exercise using continuous shrinkage priors such as the popular horseshoe prior. The proposed Signal Adaptive Variable Selector (SAVS) approach post-processes a point estimate such as the posterior mean to group the variables into signals and nulls. The approach is completely automated and does not require specification of any tuning parameters. We carried out a comprehensive simulation study to compare the performance of the proposed SAVS approach to frequentist penalization procedures and Bayesian model selection procedures. SAVS was found to be highly competitive across all the settings considered, and was particularly found to be robust to correlated designs. We also applied SAVS to a genomic dataset with more than 20,000 covariates to illustrate its scalability.