Regression trees for longitudinal and multiresponse data

By Wei-Yin Loh, Wei Zheng. 2012

68 citations recorded by the source corpus.

Previous algorithms for constructing regression tree models for longitudinal and multiresponse data have mostly followed the CART approach. Consequently, they inherit the same selection biases and computational difficulties as CART. We propose an alternative, based on the GUIDE approach, that treats each longitudinal data series as a curve and uses chi-squared tests of the residual curve patterns to select a variable to split each node of the tree. Besides being unbiased, the method is applicable to data with fixed and random time points and with missing values in the response or predictor variables. Simulation results comparing its mean squared prediction error with that of MVPART are given, as well as examples comparing it with standard linear mixed effects and generalized estimating equation models. Conditions for asymptotic consistency of regression tree function estimates are also given.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2b90a5930703f4eb632c74ed2d2598c778a80abb

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Citations in the corpus

Fifty Years of Classification and Regression Trees (2014, background): “Loh & Zheng (2013) solved this problem by using the residual vector patterns, rather than their averages, to choose the split variables.”