2014/03/30 by Rui Zhang, Kwun Chuen Gary Chan, Zhang, Rui +1
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1403.7812
openalex publication_date 2014/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce a flexible parametric mixed effects model for correlated binary data, with parameters that can be directly interpreted as marginal odds ratios. This leads to a robust estimation equation with an optimal weighting matrix being the inverse of a genuine model-based covariance matrix. Flexible correlation structures can be imposed by correlated random effects, and correlation parameters can be estimated by solving a composite likelihood score function. Marginal parameters are consistently estimated even when the conditional parametric model is misspecified, and the robust estimation procedure has low estimation efficiency loss compared to the maximum likelihood estimation under a correct model specification. Simulations, analyses of the Madras longitudinal schizophrenia study and British social attributes panel survey were carried out to demonstrate our method.