Olaniran, O.R.; Alzahrani, A.R.R. On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression. Mathematics2023, 11, 4957.
Olaniran, O.R.; Alzahrani, A.R.R. On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression. Mathematics 2023, 11, 4957.
Olaniran, O.R.; Alzahrani, A.R.R. On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression. Mathematics2023, 11, 4957.
Olaniran, O.R.; Alzahrani, A.R.R. On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression. Mathematics 2023, 11, 4957.
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 efficiency or bias.
Keywords
Random Forest; Oracle Property; Variable Selection; Bayesian Analysis; Asymptotic Normality
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
