A Regression Tree Method for Longitudinal and Clustered Data with Multivariate Responses

TL;DR

This paper extends the RE-EM tree method to handle multivariate response data, allowing for simultaneous modeling of multiple responses with correlated random effects, demonstrated through simulations and real-world poverty data analysis.

Contribution

The paper introduces a multivariate extension of the RE-EM tree, enabling joint modeling of multiple responses with correlated effects, improving over univariate approaches.

Findings

Multivariate RE-EM tree outperforms univariate methods in simulations.

The method effectively analyzes multidimensional poverty data.

Correlation between responses is incorporated into the model.

Abstract

RE-EM tree is a tree-based method that combines the regression tree and the linear mixed effects model for modeling univariate response longitudinal or clustered data. In this paper, we generalize the RE-EM tree method to multivariate response data, by adopting the Multivariate Regression Tree method proposed by De'Ath [2002]. The Multivariate RE-EM tree method estimates a population-level single tree structure that is driven by the multiple responses simultaneously and object-level random effects for each response variable, where correlation between the response variables and between the associated random effects are each allowed. Through simulation studies, we verify the advantage of the Multivariate RE-EM tree over the use of multiple univariate RE-EM trees and the Multivariate Regression Tree. We apply the Multivariate RE-EM tree to analyze a real data set that contains multidimensional nonfinancial characteristics of poverty of different countries as responses, and various potential causes of poverty as predictors.