Title
Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
article
Publication
Amsterdam ,
Subject
Mathematics
Computer. Automation
Source (journal)
Computational statistics and data analysis. - Amsterdam
Volume/pages
71(2014) , p. 667-680
ISSN
0167-9473
ISI
000328869000050
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Penalized generalized estimating equations with Elastic Net or L2-Smoothly Clipped Absolute Deviation penalization are proposed to simultaneously select the most important variables and estimate their effects for longitudinal Gaussian data when multicollinearity is present. The method is able to consistently select and estimate the main effects even when strong correlations are present. In addition, the potential pitfall of time-dependent covariates is clarified. Both asymptotic theory and simulation results reveal the effectiveness of penalization as a data mining tool for longitudinal data, especially when a large number of variables is present. The method is illustrated by mining for the main determinants of life expectancy in Europe. (C) 2013 Elsevier B.V. All rights reserved.
E-info
https://repository.uantwerpen.be/docman/iruaauth/4b0a3d/4016557.pdf
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