Jackknifing in generalized linear models
type: Item
instance of: scholarly article
title: Jackknifing in generalized linear models
zbMATH Open document ID: 0777.62073
DOI: 10.1007/BF00053397
author: Jun Shao
published in: Annals of the Institute of Statistical Mathematics
publication date: 1993-08-25T00:00:00Z
review text: For a generalized linear model, the author proves that the jackknife estimator and the corresponding jackknife studentized statistic of the covariance matrix of the maximum likelihood estimator are consistent and asymptotically normal, respectively. Furthermore, these results are shown to be true even if there exist unequal dispersion parameters in the model. For saving computation the author also discusses how to accelerate computation of the estimate.
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62E20
zbMATH DE Number: 370608
zbMATH Keywords: variance estimator
zbMATH Keywords: substitution
zbMATH Keywords: linearization
zbMATH Keywords: asymptotic covariance matrix
zbMATH Keywords: consistency
zbMATH Keywords: robustness
zbMATH Keywords: asymptotic normality
zbMATH Keywords: generalized linear model
zbMATH Keywords: jackknife estimator
zbMATH Keywords: jackknife studentized statistic
zbMATH Keywords: covariance matrix
zbMATH Keywords: maximum likelihood estimator
zbMATH Keywords: unequal dispersion parameters
reviewed by: Songgui Wang
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf00053397
OpenAlex ID: W2031628963
description: scientific article; zbMATH DE number 370608