summary:The paper investigates generalized linear models (GLM's) with binary responses such as the logistic, probit, log-log, complementary log-log, scobit and power logit models. It introduces a median estimator of the underlying structural parameters of these models based on statistically smoothed binary responses. Consistency and asymptotic normality of this estimator are proved. Examples of derivation of the asymptotic covariance matrix under the above mentioned models are presented. Finally some comments concerning a method called enhancement and robustness of median estimator are given and results of simulation experiment comparing behavior of median estimator with other robust estimators for GLM's known from the literature are report...
We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-e...
This paper presents an integrated framework for estimation and inference from generalized linear mod...
Generalized linear models have become the most commonly used class of regression models in the analy...
summary:The paper investigates generalized linear models (GLM's) with binary responses such as the l...
In this paper we propose a family of robust estimators for generalized linear models. The basic idea...
In this paper we consider a suitable scale adjustment of the estimating function which de.nes a clas...
In order to combat multicollinearity, the r–d class estimator was introduced in linear and binary lo...
Maximum likelihood estimates (MLE) of regression parameters in the generalized linear models (GLM) a...
In this article, estimation methods of the semiparametric generalized linear model known as the gene...
Abstract: Generalized Linear Models (GLMs) are a popular class of regression models when the respons...
Typically, small samples have always been a problem for binomial generalized linear models. Though g...
Typically, small samples have always been a problem for binomial generalized linear models. Though g...
In this article we consider robust generalized estimating equations for the analysis of semiparametr...
Adjusted responses, adjusted fitted values and adjusted residuals are known to play in Generalized L...
AbstractIn the framework of generalized linear models, the nonrobustness of classical estimators and...
We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-e...
This paper presents an integrated framework for estimation and inference from generalized linear mod...
Generalized linear models have become the most commonly used class of regression models in the analy...
summary:The paper investigates generalized linear models (GLM's) with binary responses such as the l...
In this paper we propose a family of robust estimators for generalized linear models. The basic idea...
In this paper we consider a suitable scale adjustment of the estimating function which de.nes a clas...
In order to combat multicollinearity, the r–d class estimator was introduced in linear and binary lo...
Maximum likelihood estimates (MLE) of regression parameters in the generalized linear models (GLM) a...
In this article, estimation methods of the semiparametric generalized linear model known as the gene...
Abstract: Generalized Linear Models (GLMs) are a popular class of regression models when the respons...
Typically, small samples have always been a problem for binomial generalized linear models. Though g...
Typically, small samples have always been a problem for binomial generalized linear models. Though g...
In this article we consider robust generalized estimating equations for the analysis of semiparametr...
Adjusted responses, adjusted fitted values and adjusted residuals are known to play in Generalized L...
AbstractIn the framework of generalized linear models, the nonrobustness of classical estimators and...
We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-e...
This paper presents an integrated framework for estimation and inference from generalized linear mod...
Generalized linear models have become the most commonly used class of regression models in the analy...