BeSS: An R Package for Best Subset Selection in Linear, Logistic and CoxPH Models

2017/09/19 by Canhong Wen, Aijun Zhang, Wen, Canhong +5 · 1 citation
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Bayesian Modeling and Causal Inference #Computation (stat.CO) #FOS: Computer and information sciences #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1709.06254

openalex publication_date 2017/09/19 · openalex created_date 2017/10/06 · openalex updated_date 2026/07/28

Abstract

We introduce a new R package, BeSS, for solving the best subset selection problem in linear, logistic and Cox's proportional hazard (CoxPH) models. It utilizes a highly efficient active set algorithm based on primal and dual variables, and supports sequential and golden search strategies for best subset selection. We provide a C++ implementation of the algorithm using Rcpp interface. We demonstrate through numerical experiments based on enormous simulation and real datasets that the new BeSS package has competitive performance compared to other R packages for best subset selection purpose.

Citations

• Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
• A Constructive Approach to High-dimensional Regression

Cited by

• Image denoising via K-SVD with primal-dual active set algorithm

Related

• COMBSS: Best Subset Selection via Continuous Optimization
• Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique
• Best Subset Selection in Reduced Rank Regression
• 10,000+ Times Accelerated Robust Subset Selection (ARSS)
• Robust Best Subset Selection via Fast Approximate MM-Estimation