On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression

Oyebayo Ridwan Olaniran () and Ali Rashash R. Alzahrani
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Oyebayo Ridwan Olaniran: Department of Statistics, Faculty of Physical Sciences, University of Ilorin, llorin 240101, Nigeria
Ali Rashash R. Alzahrani: Mathematics Department, Faculty of Sciences, Umm Al-Qura University, Makkah 24382, Saudi Arabia

Mathematics, 2023, vol. 11, issue 24, 1-29

Abstract: Random forest (RF) is a widely used data prediction and variable selection technique. However, the variable selection aspect of RF can become unreliable when there are more irrelevant variables than relevant ones. In response, we introduced the Bayesian random forest (BRF) method, specifically designed for high-dimensional datasets with a sparse covariate structure. Our research demonstrates that BRF possesses the oracle property, which means it achieves strong selection consistency without compromising the efficiency or bias.

Keywords: random forest; oracle property; variable selection; Bayesian analysis; asymptotic normality (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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