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Pfanzagl and Wefelmeyer (1978) show that bias corrected ML estimators are higher order efficient. Their procedure however is computationally complicated because it requires integrating complicat...
Models with multiple change points are used in many fields; however, the theoretical properties of maximum likelihood estimators of such models have received relatively little attention. The goal of t...
We introduce the dispersion models with a regression structure to extend the generalized linear models, the exponential family nonlinear models (Cordeiro and Paula, 1989) and the proper dispersion m...
Bias Corrected Maximum Likelihood Estimators in Nonlinear Overdispersed Models
This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology an...

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