title: Diagnosis of random-effect model misspecification in generalized linear mixed models for binary response.
abstract: 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.
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA. huang@stat.sc.edu
authors.forename: Xianzheng
authors.initials: X
authors.lastname: Huang