Diagnosis of random-effect model misspecification in generalized linear mixed models for binary response |
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Authors: | Huang Xianzheng |
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Institution: | Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, U.S.A. email:;  |
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Abstract: | Summary . Generalized linear mixed models (GLMMs) are widely used in the analysis of clustered data. However, the validity of likelihood-based inference in such analyses can be greatly affected by the assumed model for the random effects. We propose a diagnostic method for random-effect model misspecification in GLMMs for clustered binary response. We provide a theoretical justification of the proposed method and investigate its finite sample performance via simulation. The proposed method is applied to data from a longitudinal respiratory infection study. |
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Keywords: | Clustered binary response Generalized linear mixed models Random effects |
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