论文标题
带有多元响应的纵向和聚类数据的回归树方法
A Regression Tree Method for Longitudinal and Clustered Data with Multivariate Responses
论文作者
论文摘要
Re-Em树是一种基于树的方法，它结合了回归树和用于建模单变量响应纵向或聚类数据的线性混合效应模型。在本文中，我们通过采用De'Ath [2002]提出的多元回归树方法，将Re-Em树方法推广到多元响应数据。多元RE-EM树方法估计了一个人群级的单树结构，该结构由每个响应变量同时由多个响应和对象级随机效应驱动，其中响应变量之间的相关性以及相关的随机效应都允许。通过仿真研究，我们验证了多元RE-EM树在使用多个单变量Re-Em树和多元回归树上的优势。我们应用多元重新EM树来分析一个真实的数据集，该数据集包含不同国家贫困的多维非财务特征，作为反应，以及各种潜在的贫困原因作为预测因素。
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.
