Opuscula Mathematica
Opuscula Math. 32, no. 4 (), 761-774
Opuscula Mathematica

On the maximum likelihood estimator in the generalized beta regression model

Abstract. The subject of this article is to present the beta - regression model, where we assume that one parameter in the model is described as a combination of algebraically independent continuous functions. The proposed beta model is useful when the dependent variable is continuous and restricted to the bounded interval. The parameters are obtained by maximum likelihood estimation. We prove that estimators are consistent and asymptotically normal.
Keywords: nonlinear regression, beta distribution, scale parameter, shape parameter, maximum likelihood estimator.
Mathematics Subject Classification: 62J02, 62F10.
Cite this article as:
Jerzy P. Rydlewski, Dominik Mielczarek, On the maximum likelihood estimator in the generalized beta regression model, Opuscula Math. 32, no. 4 (2012), 761-774, http://dx.doi.org/10.7494/OpMath.2012.32.4.761
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ISSN 1232−9274, e-ISSN 2300−6919, DOI https://doi.org/10.7494/OpMath
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