Opuscula Math. 32, no. 4 (2012), 761-774
http://dx.doi.org/10.7494/OpMath.2012.32.4.761

 
Opuscula Mathematica

On the maximum likelihood estimator in the generalized beta regression model

Jerzy P. Rydlewski
Dominik Mielczarek

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.

Full text (pdf)

Opuscula Mathematica - cover

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

Download this article's citation as:
a .bib file (BibTeX),
a .ris file (RefMan),
a .enw file (EndNote)
or export to RefWorks.

In accordance with EU legislation we advise you this website uses cookies to allow us to see how the site is used. All data is anonymized.
All recent versions of popular browsers give users a level of control over cookies. Users can set their browsers to accept or reject all, or certain, cookies.