Generalized bootstrap for estimating equations
نویسندگان
چکیده
منابع مشابه
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...
متن کاملGeneralized Bootstrap for Estimating Equations by Snigdhansu Chatterjee
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 delete-d 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 re...
متن کاملThe cluster bootstrap consistency in generalized estimating equations
The cluster bootstrap resamples clusters or subjects instead of individual observations in order to preserve the dependence within each cluster or subject. In this paper, we provide a theoretical justification of using the cluster bootstrap for the inferences of the generalized estimating equations (GEE) for clustered/longitudinal data. Under the general exchangeable bootstrap weights, we show ...
متن کاملGeneralized 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...
متن کاملA 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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2005
ISSN: 0090-5364
DOI: 10.1214/009053604000000904