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Covariance regression with random forests.

Cansu Alakus1, Denis Larocque2, Aurélie Labbe2

  • 1Department of Decision Sciences, HEC Montréal, Montréal, Canada. cansu.alakus@hec.ca.

BMC Bioinformatics
|June 17, 2023
PubMed
Summary
This summary is machine-generated.

We introduce Covariance Regression with Random Forests (CovRegRF), a novel method for estimating covariance matrices in multivariate responses. This approach accurately models relationships between variables and covariates, with applications in biomedicine and epidemiology.

Keywords:
Covariance regressionMultivariate responseRandom forestsVariable importance

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Area of Science:

  • Biostatistics
  • Machine Learning
  • Genomics

Background:

  • Estimating conditional covariances among multivariate responses is crucial in fields like neuroscience, epidemiology, and biomedicine.
  • Existing methods may not fully capture complex relationships influenced by covariates.

Purpose of the Study:

  • To propose a novel method, Covariance Regression with Random Forests (CovRegRF), for estimating covariance matrices of multivariate responses conditional on covariates.
  • To develop a significance test for the partial effect of covariates on the covariance structure.

Main Methods:

  • CovRegRF utilizes a random forest framework with a specialized splitting rule designed to maximize differences in sample covariance estimates between child nodes.
  • The method builds decision trees to partition data based on covariates, enabling conditional covariance estimation.

Main Results:

  • Simulation studies demonstrate that CovRegRF provides accurate covariance matrix estimates.
  • The proposed significance test effectively controls Type-1 error rates.
  • The method was successfully applied to thyroid disease data.

Conclusions:

  • CovRegRF offers a robust and accurate approach for modeling conditional covariances in multivariate data.
  • The method and its significance test are valuable tools for analyzing complex biological and medical data.
  • An R package for CovRegRF is available on CRAN.