Covariance regression with random forests. (arXiv:2209.08173v2 [stat.ME] UPDATED)

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 …

arxiv covariance random random forests regression