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On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression.
- Source :
- Mathematics (2227-7390); Dec2023, Vol. 11 Issue 24, p4957, 29p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- RANDOM forest algorithms
ASYMPTOTIC normality
BAYESIAN analysis
Subjects
Details
- Language :
- English
- ISSN :
- 22277390
- Volume :
- 11
- Issue :
- 24
- Database :
- Complementary Index
- Journal :
- Mathematics (2227-7390)
- Publication Type :
- Academic Journal
- Accession number :
- 174461107
- Full Text :
- https://doi.org/10.3390/math11244957