Computational Statistics

, Volume 33, Issue 2, pp 983–996 | Cite as

Working correlation structure selection in generalized estimating equations

Original Paper
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Abstract

Selecting an appropriate correlation structure in analyzing longitudinal data can greatly improve the efficiency of parameter estimation, which leads to more reliable statistical inference. A number of such criteria have been proposed in the literature from different perspectives. However, little is known about the relative performance of these criteria. We review and evaluate these criteria by carrying out extensive simulation studies. Surprisingly, we find that the AIC and the BIC based on either the Gaussian working likelihood or the empirical likelihood outperform the others.

Keywords

Correlation information criterion Empirical likelihood Longitudinal data Model selection 
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Notes

Acknowledgements

This research was funded by the Australian Research Council Discovery Projects (DP130100766 and DP160104292). L. Fu’s research was partly supported by the National Science Foundation of China (Grant Nos. 11201365 and 11301408) and the Doctoral Programs Foundation of Ministry of Education of China (Grant No. 2012020112005)and the Fundamental Research Funds for the Central Universities (Grant No. xjj2017180).

Supplementary material

180_2018_800_MOESM1_ESM.pdf (280 kb)
Supplementary material 1 (pdf 280 KB)
180_2018_800_MOESM2_ESM.r (40 kb)
Supplementary material 2 (R 39 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsXi’an Jiaotong UniversityXi’anChina
  2. 2.Logistics Research CenterShanghai Maritime UniversityShanghaiChina
  3. 3.School of Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia

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