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...

In this thesis, we propose to analyze panel count data using a spline-based sieve generalized estimating equation method with a semiparametric proportional mean model E(N(t)|Z) = Λ0 (t) e T 0 Z . The natural log of the baseline mean function, logΛ0 (t), is approximated by a monotone cubic B-spline function. The estimates of regression parameters and spline coefficients are the roots of the spli...

each semester, students are asked to evaluate the academic staff through an online questionnaire. generalized estimating equations model (gee), taking into account the correlation between scores, is the established tool to analyze longitudinal data. the aim of this manuscript is to identify characteristics that influence staff score and to address the importance of selection of appropriate corr...

A local likelihood estimator for a nonparametric nuisance function is proposed in the context of semiparametric skew-normal distributions. Constraints imposed on such functions result in a nonparametric estimator with a different target function for maximization from classical local likelihood estimators. The optimal asymptotic semiparametric efficiency bound on parameters of interest is achiev...

Researchers are often interested in analyzing data that arise from a longitudinal or clustered design. Although there are a variety of standard likelihood-based approaches to analysis when the outcome variables are approximately multivariate normal, models for discrete-type outcomes generally require a different approach. Liang and Zeger formalized an approach to this problem using generalized ...

In [12], it was shown that a generalized maximum likelihood estimation problem on a (canonical) α-power-law model (M-family) can be solved by solving a system of linear equations. This was due to an orthogonality relationship between the M-family and a linear family with respect to the relative α-entropy (or the Iα-divergence). Relative α-entropy is a generalization of the usual relative entrop...

This article discusses extensions of generalized linear models for the analysis of longitudinal data. Two approaches are considered: subject-specific (SS) models in which heterogeneity in regression parameters is explicitly modelled; and population-averaged (PA) models in which the aggregate response for the population is the focus. We use a generalized estimating equation approach to fit both ...