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Comparative GMM and GQL logistic regression models on hierarchical data

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  • Bei Wang
  • Jeffrey R. Wilson

Abstract

We often rely on the likelihood to obtain estimates of regression parameters but it is not readily available for generalized linear mixed models (GLMMs). Inferences for the regression coefficients and the covariance parameters are key in these models. We presented alternative approaches for analyzing binary data from a hierarchical structure that do not rely on any distributional assumptions: a generalized quasi-likelihood (GQL) approach and a generalized method of moments (GMM) approach. These are alternative approaches to the typical maximum-likelihood approximation approach in Statistical Analysis System (SAS) such as Laplace approximation (LAP). We examined and compared the performance of GQL and GMM approaches with multiple random effects to the LAP approach as used in PROC GLIMMIX, SAS. The GQL approach tends to produce unbiased estimates, whereas the LAP approach can lead to highly biased estimates for certain scenarios. The GQL approach produces more accurate estimates on both the regression coefficients and the covariance parameters with smaller standard errors as compared to the GMM approach. We found that both GQL and GMM approaches are less likely to result in non-convergence as opposed to the LAP approach. A simulation study was conducted and a numerical example was presented for illustrative purposes.

Suggested Citation

  • Bei Wang & Jeffrey R. Wilson, 2018. "Comparative GMM and GQL logistic regression models on hierarchical data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 409-425, February.
  • Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:409-425
    DOI: 10.1080/02664763.2017.1279595
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Jowaheer, Vandna & Sutradhar, Brajendra, 2009. "GMM versus GQL inferences for panel count data," Statistics & Probability Letters, Elsevier, vol. 79(18), pages 1928-1934, September.
    3. Yoonsang Kim & Young-Ku Choi & Sherry Emery, 2013. "Logistic Regression With Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages," The American Statistician, Taylor & Francis Journals, vol. 67(3), pages 171-182, August.
    4. H. Zhang & Q. Yu & C. Feng & D. Gunzler & P. Wu & X. M. Tu, 2012. "A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 2067-2079, June.
    5. Zhiguo Xiao & Jun Shao & Mari Palta, 2010. "GMM in linear regression for longitudinal data with multiple covariates measured with error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 791-805.
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