Covariance matrix of the bias-corrected maximum likelihood estimator in generalized linear models

Gauss Cordeiro (), Denise Botter (), Alexsandro Cavalcanti () and Lúcia Barroso ()

Statistical Papers, 2014, vol. 55, issue 3, 643-652

Abstract: For the first time, we obtain a general formula for the $$n^{-2}$$ asymptotic covariance matrix of the bias-corrected maximum likelihood estimators of the linear parameters in generalized linear models, where $$n$$ is the sample size. The usefulness of the formula is illustrated in order to obtain a better estimate of the covariance of the maximum likelihood estimators and to construct better Wald statistics. Simulation studies and an application support our theoretical results. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Bias estimator; Covariance matrix; Generalized linear model; Wald test; 62Fxx; 62F12 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s00362-013-0514-1

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