Tsou (2003a) proposed a parametric procedure for making robust inference for mean regression parameters in the context of generalized linear models. This robust procedure is extended to model variance heterogeneity. The normal working model is adjusted to become asymptotically robust for inference about regression parameters of the variance function for practically all continuous response variables. The connection between the novel robust variance regression model and the estimating equations approach is also provided.Generalized linear models, variance function, robust profile likelihood, normal regression,
In this article we consider robust generalized estimating equations for the analysis of semiparametr...
SIGLEAvailable from British Library Document Supply Centre-DSC:DX188726 / BLDSC - British Library Do...
Generalized Linear Models extends classical regression models to non-normal response variables and a...
The purpose of this research is to propose a robust estimate for the parameters of a nonlinear regre...
In this paper we propose a family of robust estimators for generalized linear models. The basic idea...
In many situations, data follow a generalized partly linear model in which the mean of the responses...
By starting from a natural class of robust estimators for generalized linear models based on the not...
In this paper we consider a suitable scale adjustment of the estimating function which de.nes a clas...
By starting from a natural class of robust estimators for generalized linear models based on the not...
Generalized linear models have become the most commonly used class of regression models in the analy...
AbstractIn the framework of generalized linear models, the nonrobustness of classical estimators and...
Generalized linear models (McCullagh and Nelder 1989) are a popular technique for modeling a large v...
© 2014 Royal Statistical Society. We consider heteroscedastic regression models where the mean funct...
Generalized linear models (McCullagh and Nelder 1989) are a popular technique for modeling a large v...
In the framework of generalized linear models, the nonrobustness of classical estimators and tests f...
In this article we consider robust generalized estimating equations for the analysis of semiparametr...
SIGLEAvailable from British Library Document Supply Centre-DSC:DX188726 / BLDSC - British Library Do...
Generalized Linear Models extends classical regression models to non-normal response variables and a...
The purpose of this research is to propose a robust estimate for the parameters of a nonlinear regre...
In this paper we propose a family of robust estimators for generalized linear models. The basic idea...
In many situations, data follow a generalized partly linear model in which the mean of the responses...
By starting from a natural class of robust estimators for generalized linear models based on the not...
In this paper we consider a suitable scale adjustment of the estimating function which de.nes a clas...
By starting from a natural class of robust estimators for generalized linear models based on the not...
Generalized linear models have become the most commonly used class of regression models in the analy...
AbstractIn the framework of generalized linear models, the nonrobustness of classical estimators and...
Generalized linear models (McCullagh and Nelder 1989) are a popular technique for modeling a large v...
© 2014 Royal Statistical Society. We consider heteroscedastic regression models where the mean funct...
Generalized linear models (McCullagh and Nelder 1989) are a popular technique for modeling a large v...
In the framework of generalized linear models, the nonrobustness of classical estimators and tests f...
In this article we consider robust generalized estimating equations for the analysis of semiparametr...
SIGLEAvailable from British Library Document Supply Centre-DSC:DX188726 / BLDSC - British Library Do...
Generalized Linear Models extends classical regression models to non-normal response variables and a...