Estimating Equations for Estimation of Mcdonald Generalized Beta— Binomial Parameters

Estimation of Parameters in the Beta Binomial Model

Generalized Bootstrap for Estimating Equations

We introduce a generalized bootstrap technique for estimators obtained by solving estimating equations. Some special cases of this generalized bootstrap are the classical bootstrap of Efron, the deleted jackknife and variations of the Bayesian bootstrap. The use of the proposed technique is discussed in some examples. Distributional consistency of the method is established and an asymptotic rep...

Individual-specific, sparse inverse covariance estimation in generalized estimating equations

This paper proposes a data-driven approach that derives individual-specific sparse working correlation matrices for generalized estimating equations (GEEs). The approach is motivated by the observation that, in some applications of the GEE, the covariance structure across individuals is heterogeneous and cannot be appropriately captured by a single correlationmatrix. The proposed approach enjoy...

Generalized Estimating Equations

It is a privilege to be able to peruse the fine article by Ziegler and Vens [1], as well as the contributions made by nine discussants [2]. Over the last 25 years, generalized estimating equations (GEE) have seen an ever further spreading use. Nonetheless, it is a technique confronted with confusion and, at times, misunderstanding. The user must carefully read the technique’s manual. Let us hig...

A Generalized Estimating Equations

When inferences focus on population averages, one can directly model all of the marginal expectations E(Yij) = μij in terms of covariates of interest. This is typically done via h(μij) = x ′ ijβ, with h(·) some known link function, such as the logit link for binary responses. The marginal variance depends on the marginal mean according to Var(Yij) = v(μij)φ, where v(·) is a known variance funct...