2018/10/21 by Pallavi Ray, Anirban Bhattacharya, Ray, Pallavi +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Decision Sciences · #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic and phenotypic traits in livestock #Methodology (stat.ME) #Optimal Experimental Design Methods
paper · pdf · doi:10.48550/arxiv.1810.09004
openalex publication_date 2018/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.