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제목: Regression Trees for Longitudinal and Multiresponse Data
연사: Wei-Yin Loh (Department of Statistics, University of Wisconsin, Madison)
장소: SUNY Korea Academic Building C, Room 156 (인천광역시 연수구 송도동 187, Tel: 032-626-1114 )
일시: 7월24일(화) 오후4시
문의: Hongshik Ahn 교수 (hahn@sunykorea.ac.kr, 032-626-1010)
 
초록: 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.