A0193
Title: Covariance regression with random forests
Authors: Denis Larocque - HEC Montreal (Canada) [presenting]
Cansu Alakus - HEC Montreal (Canada)
Aurelie Labbe - HEC Montreal (Canada)
Abstract: 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. We also demonstrate an application of the proposed method with a thyroid disease data set. CovRegRF is implemented in a freely available R package on CRAN.
