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Random-intercept misspecification in generalized linear mixed models for binary responses

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  • Shun Yu

    (Wells Fargo & Company)

  • Xianzheng Huang

    (University of South Carolina)

Abstract

We study properties of maximum likelihood estimators of parameters in generalized linear mixed models for a binary response in the presence of random-intercept model misspecification. Further exploiting the test proposed in an existing work initially designed for detecting general random-effects misspecification, we are able to reveal how the true random-intercept distribution deviates from the assumed. Besides this advance compared to the existing methods, we also provide theoretical insights on when and why the proposed test has low power to identify certain forms of misspecification. Large-sample numerical study and finite-sample simulation experiments are carried out to illustrate the theoretical findings.

Suggested Citation

  • Shun Yu & Xianzheng Huang, 2017. "Random-intercept misspecification in generalized linear mixed models for binary responses," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 333-359, August.
    • Handle: RePEc:spr:stmapp:v:26:y:2017:i:3:d:10.1007_s10260-017-0376-0
    DOI: 10.1007/s10260-017-0376-0
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    References listed on IDEAS

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    Cited by:

    1. Shun Yu & Xianzheng Huang, 2019. "Link misspecification in generalized linear mixed models with a random intercept for binary responses," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 827-843, September.

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