Journal
ANNALS OF APPLIED STATISTICS
Volume 7, Issue 1, Pages 495-522Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/12-AOAS596
Keywords
CART; decision tree; generalized estimating equation; linear mixed effects model; lowess; missing values; recursive partitioning; selection bias
Categories
Funding
- U.S. Army Research Office [W911NF-09-1-0205]
- National Institutes of Health [P50CA143188]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available