Addressing the Variable Selection Bias and Local Optimum Limitations of Longitudinal Recursive Partitioning with Time-Efficient Approximations

Addressing the Variable Selection Bias and Local Optimum Limitations of Longitudinal Recursive Partitioning with Time-Efficient Approximations

abstract: Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together i...

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Bibliographic Details
Other Authors: Stegmann, Gabriela (Author)
Format: Doctoral Thesis
Language:English
Published: 2019
Subjects:
Quantitative psychology
Growth Curve Model
Longitudinal Data
Machine Learning
Mixed-Effects Models
Recursive Partitioning
Regression Trees
Online Access:http://hdl.handle.net/2286/R.I.54792

Internet

http://hdl.handle.net/2286/R.I.54792

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