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.
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Acknowledgments
We gratefully acknowledge the partial financial support of the following Brazilian agencies: CNPq and FAPESP.
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Cordeiro, G.M., Botter, D.A., Cavalcanti , A.B. et al. Covariance matrix of the bias-corrected maximum likelihood estimator in generalized linear models. Stat Papers 55, 643–652 (2014). https://doi.org/10.1007/s00362-013-0514-1
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DOI: https://doi.org/10.1007/s00362-013-0514-1