2022/09/16 by Cansu Alakuş, Alakus, Cansu, Denis Larocque +3 · 1 citation
Computer Science · Mathematics · #Analysis of covariance #Applications (stat.AP) #Artificial intelligence #Bayesian Modeling and Causal Inference #Computer science #Covariance #Covariance matrix #Covariate #Estimation of covariance matrices #FOS: Computer and information sciences #Machine Learning (stat.ML) #Mathematics #Methodology (stat.ME) #Multivariate statistics #Random forest #Statistics
paper · pdf · doi:10.48550/arxiv.2209.08173
openalex publication_date 2022/09/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine. We propose a new method called Covariance Regression with Random Forests (CovRegRF) to estimate the covariance matrix of a multivariate response given a set of covariates, using a random forest framework. Random forest trees are built with a splitting rule specially designed to maximize the difference between the sample covariance matrix estimates of the child nodes. We also propose a significance test for the partial effect of a subset of covariates. We evaluate the performance of the proposed method and significance test through a simulation study which shows that the proposed method provides accurate covariance matrix estimates and that the Type-1 error is well controlled. An application of the proposed method to thyroid disease data is also presented. CovRegRF is implemented in a freely available R package on CRAN.