Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations

A. Blommaert, N. Hens and Ph. Beutels

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 667-680

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.

Keywords: Covariate selection; Generalized estimating equations; Longitudinal data; Multicollinearity; Penalization; Time-dependent covariates (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:667-680

DOI: 10.1016/j.csda.2013.02.023

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