Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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.
Czasopismo
Rocznik
Tom
Strony
761--774
Opis fizyczny
Bibliogr. 9 poz., rys.
Twórcy
autor
autor
- AGH University of Science and Technology Faculty of Applied Mathematics al. A. Mickiewicza 30, 30-059 Krakow, Poland, ry@agh.edu.pl
Bibliografia
- [1] F. Cribari–Neto, K.L.P. Vasconcellos, Nearly unbiased maximum likelihood estimation for the beta distribution, J. Stat. Comput. Sim. 72 (2002), 107–118.
- [2] L. Fahrmeir, H. Kaufmann, Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models, Ann. Statist. 13 (1985), 342–368.
- [3] S.L.P. Ferrari, F. Cribari-Neto, Beta regression for modelling rates and proportions, J. Appl. Stat. 31 (2004) 7, 799–815.
- [4] J.P. Rydlewski, Beta–regression model for periodic data with a trend, Univ. Iagel. Acta Math. XLV (2007), 211–222.
- [5] J.P. Rydlewski, A note on the maximum likelihood estimator in the gamma regression model, Opuscula Math. 29 (2009) 3, 305–312.
- [6] G.A.F. Seber, C.J. Wild, Nonlinear Regression, Wiley, New York, 2003.
- [7] A.B. Simas, W. Barreto–Souza, A.V. Rocha, Improved estimators for a general class of beta regression models, Comput. Stat. Data Anal. 54 (2010), 348–366.
- [8] R.W.M. Wedderburn, On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models, Biometrika 63 (1976), 27–32.
- [9] B.C. Wei, Exponential Family Nonlinear Models, Springer, Singapore, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-AGHS-0007-0011