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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Published on: September 17, 2019

Penalized generalized estimating equations for high-dimensional longitudinal data analysis.

Lan Wang1, Jianhui Zhou, Annie Qu

  • 1School of Statistics, University of Minnesota, 224 Church Street Southeast, Minneapolis, Minnesota 55455, USA. wangx346@umn.edu

Biometrics
|September 30, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces penalized generalized estimating equations (GEEs) for high-dimensional longitudinal data analysis. The method offers robust model selection, even with misspecified correlation structures, improving analysis in complex biological and health studies.

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Area of Science:

  • Statistics
  • Biostatistics
  • Genomics

Background:

  • High-dimensional longitudinal data analysis is crucial for fields like genomics and health studies.
  • Existing regression methods often assume data independence and rely on complex likelihood functions.
  • Constructing likelihood functions for high-dimensional, correlated discrete data is challenging.

Purpose of the Study:

  • To develop a penalized generalized estimating equations (GEEs) procedure for high-dimensional longitudinal data.
  • To establish asymptotic theory for this procedure in a high-dimensional framework.
  • To ensure model selection consistency, even with misspecified correlation structures.

Main Methods:

  • Utilized penalized generalized estimating equations (GEEs).
  • Developed a high-dimensional asymptotic theory where the number of covariates p(n) scales with the number of clusters n.
  • Required only specification of the first two marginal moments and a working correlation structure.

Main Results:

  • Established asymptotic theory for penalized GEEs in high-dimensional settings.
  • Demonstrated consistency of model selection irrespective of working correlation structure misspecification.
  • Evaluated performance via Monte Carlo simulations.

Conclusions:

  • The penalized GEE procedure provides a robust method for analyzing high-dimensional longitudinal data.
  • The method is applicable to complex datasets, such as gene expression data.
  • It overcomes limitations of existing methods by not requiring a joint likelihood function and handling correlation misspecification.