Bias-corrected estimators for dispersion models with dispersion covariates

Abstract

In this paper we discuss bias-corrected estimators for the regression and the dispersion parameters in an extended class of dispersion models ( Jørgensen, 1997b). This class extends the regular dispersion models by letting the dispersion parameter vary throughout the observations, and contains the dispersion models as particular case. General formulae for the O ( n − 1 ) bias are obtained explicitly in dispersion models with dispersion covariates, which generalize previous results obtained by Botter and Cordeiro (1998), Cordeiro and McCullagh (1991), Cordeiro and Vasconcellos (1999), and Paula (1992). The practical use of the formulae is that we can derive closed-form expressions for the O ( n − 1 ) biases of the maximum likelihood estimators of the regression and dispersion parameters when the information matrix has a closed-form. Various expressions for the O ( n − 1 ) biases are given for special models. The formulae have advantages for numerical purposes because they require only a supplementary weighted linear regression. We also compare these bias-corrected estimators with two different estimators which are also bias-free to order O ( n − 1 ) that are based on bootstrap methods. These estimators are compared by simulation.