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Working correlation structure selection in generalized estimating equations

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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.

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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).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

    Liya Fu

  2. Logistics Research Center, Shanghai Maritime University, Shanghai, China

    Yangyang Hao

  3. School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia

    You-Gan Wang

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  1. Liya Fu
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  2. Yangyang Hao
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  3. You-Gan Wang
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Correspondence to You-Gan Wang.

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Fu, L., Hao, Y. & Wang, YG. Working correlation structure selection in generalized estimating equations. Comput Stat 33, 983–996 (2018). https://doi.org/10.1007/s00180-018-0800-4

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