On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression
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 desig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/24/4957 |
| _version_ | 1797380114520997888 |
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| author | Oyebayo Ridwan Olaniran Ali Rashash R. Alzahrani |
| author_facet | Oyebayo Ridwan Olaniran Ali Rashash R. Alzahrani |
| author_sort | Oyebayo Ridwan Olaniran |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T20:33:39Z |
| format | Article |
| id | doaj.art-2c1eff6eca8941dc97e564cdc44d3e9a |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T20:33:39Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2c1eff6eca8941dc97e564cdc44d3e9a2023-12-22T14:23:25ZengMDPI AGMathematics2227-73902023-12-011124495710.3390/math11244957On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian RegressionOyebayo Ridwan Olaniran0Ali Rashash R. Alzahrani1Department of Statistics, Faculty of Physical Sciences, University of Ilorin, llorin 240101, NigeriaMathematics Department, Faculty of Sciences, Umm Al-Qura University, Makkah 24382, Saudi ArabiaRandom 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.https://www.mdpi.com/2227-7390/11/24/4957random forestoracle propertyvariable selectionBayesian analysisasymptotic normality |
| spellingShingle | Oyebayo Ridwan Olaniran Ali Rashash R. Alzahrani On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression Mathematics random forest oracle property variable selection Bayesian analysis asymptotic normality |
| title | On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression |
| title_full | On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression |
| title_fullStr | On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression |
| title_full_unstemmed | On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression |
| title_short | On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression |
| title_sort | on the oracle properties of bayesian random forest for sparse high dimensional gaussian regression |
| topic | random forest oracle property variable selection Bayesian analysis asymptotic normality |
| url | https://www.mdpi.com/2227-7390/11/24/4957 |
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