This paper studies moderate deviation behaviors of the generalized method of moments and generalized empirical likelihood estimators for generalized estimating equations, where the number of equations can be larger than the number of unknown parameters. We consider two cases for the data generating probability measure: themodel assumption and local contaminations or deviations from the model as...

This paper studies moderate deviation behaviors of the generalized method of moments and generalized empirical likelihood estimators for generalized estimating equations, where the number of equations can be larger than the number of unknown parameters. We consider two cases for the data generating probability measure: the model assumption and local contaminations or deviations from the model a...

Modeling longitudinal binary data is challenging but common in practice. Existing methods on modeling of responses take no account the fact that correlation coefficient must have an upper bound which smaller than one. Ignoring this can lead to incorrect statistical inferences for data. A novel method proposed model mean and within-subject coefficients data, simultaneously, by taking into constr...

Model selection is a necessary step in many practical regression analyses. But for methods based on estimating equations, such as the quasi-likelihood and generalized estimating equation (GEE) approaches, there seem to be few well-studied model selection techniques. In this article, we propose a new model selection criterion that minimizes the expected predictive bias (EPB) of estimating equati...

Generalized estimating equations (GEE; Liang & Zeger, 1986) are extended to general vector regression settings. When the response vectors are of mixed type (e.g. continuous–binary response pairs), the proposed method is a semiparametric alternative to full-likelihood copula methods. When the response vectors are of the same type (e.g. physical measurements on left and right eyes), the proposed ...