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

Author

Listed:
  • Oyebayo Ridwan Olaniran

    (Department of Statistics, Faculty of Physical Sciences, University of Ilorin, llorin 240101, Nigeria
    These authors contributed equally to this work.)

  • Ali Rashash R. Alzahrani

    (Mathematics Department, Faculty of Sciences, Umm Al-Qura University, Makkah 24382, Saudi Arabia
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Oyebayo Ridwan Olaniran & Ali Rashash R. Alzahrani, 2023. "On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression," Mathematics, MDPI, vol. 11(24), pages 1-29, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4957-:d:1300387
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    References listed on IDEAS

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    1. Oyebayo Ridwan Olaniran & Aliu Omotayo Sikiru & Jeza Allohibi & Abdulmajeed Atiah Alharbi & Nada MohammedSaeed Alharbi, 2025. "Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction," Mathematics, MDPI, vol. 13(4), pages 1-25, February.

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