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Activity Number: 461 - SPEED: Machine Learning
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #324108
Title: Tree-Based Models for Longitudinal Data
Author(s): Brittany Green* and Peng Wang
Companies: University of Cincinnati and University of Cincinnati
Keywords: Longitudinal ; Decision Tree ; Estimating Equation
Abstract:

Classification and regression tree (CART) has been broadly applied due to its simplicity of explanation, automatic variable selection, visualization and interpretation. Previous algorithms for constructing CART for longitudinal data suffer from the computational difficulties in estimation of covariance matrix at each node. We proposed to utilize the quadratic inference function (QIF) and developed a new criterion, named RSSQ, to select the best splits. The proposed approach incorporates correlation wihout estimating the correlation parameters. Therefore we could improve the efficiency of the partition results and prediction accuracy.


Authors who are presenting talks have a * after their name.

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