2021/12/28 by Chuji Luo, Michael J. Daniels, Luo, Chuji +1 · 1 citation
Chemistry · Mathematics · #Advanced Statistical Methods and Models #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME) #Spectroscopy and Chemometric Analyses #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2112.13998
openalex publication_date 2021/12/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Variable selection is an important statistical problem. This problem becomes more challenging when the candidate predictors are of mixed type (e.g. continuous and binary) and impact the response variable in nonlinear and/or non-additive ways. In this paper, we review existing variable selection approaches for the Bayesian additive regression trees (BART) model, a nonparametric regression model, which is flexible enough to capture the interactions between predictors and nonlinear relationships with the response. An emphasis of this review is on the capability of identifying relevant predictors. We also propose two variable importance measures which can be used in a permutation-based variable selection approach, and a backward variable selection procedure for BART. We present simulations demonstrating that our approaches exhibit improved performance in terms of the ability to recover all the relevant predictors in a variety of data settings, compared to existing BART-based variable selection methods.