Add to ORKGAbstract. A class of robust estimators which are obtained from dual representation of φ-divergences, are studied empirically for the normal location model. Members of this class of estimators are compared, and it is found that they are efficient at the true model and offer an attractive alternative to the maximum likeli-hood, in term of robustness. Key words and phrases: Minimum divergence estimators; Efficiency; Robustness; M-estimators; Influence function. 1
Here we investigate the robustness properties of the class of minimum power divergence estimators fo...
In this note we introduce some divergence-based model selection criteria. These criteria are defined...
Robust inference based on the minimization of statistical divergences has proved to be a useful alte...
The class of dual [phi]-divergence estimators (introduced in Broniatowski and Keziou (2009) [5]) is ...
AbstractThe class of dual ϕ-divergence estimators (introduced in Broniatowski and Keziou (2009) [5])...
This paper studies the Minimum Divergence (MD) class of estimators for econometric models specified...
We introduce new robustness and efficiency measures based on divergences and use them to construct e...
A minimum divergence estimation method is developed for robust parameter estimation. The proposed ap...
summary:Point estimators based on minimization of information-theoretic divergences between empirica...
The aim of robust statistics is to develop statistical procedures which are not unduly influenced by...
This paper compares the minimum divergence estimator of Basu et al. (1998) to a competing minimum di...
The aim of robust statistics is to develop statistical procedures which are not unduly influenced by...
A robust estimator introduced by Beran (1977a, 1977b)?Mich i based on the minimum Hellinger distance...
Basu et al. (1998) proposed the minimum divergence estimating method which is free from using the pa...
AbstractIn the present work, the problem of estimating parameters of statistical models for categori...
Here we investigate the robustness properties of the class of minimum power divergence estimators fo...
In this note we introduce some divergence-based model selection criteria. These criteria are defined...
Robust inference based on the minimization of statistical divergences has proved to be a useful alte...
The class of dual [phi]-divergence estimators (introduced in Broniatowski and Keziou (2009) [5]) is ...
AbstractThe class of dual ϕ-divergence estimators (introduced in Broniatowski and Keziou (2009) [5])...
This paper studies the Minimum Divergence (MD) class of estimators for econometric models specified...
We introduce new robustness and efficiency measures based on divergences and use them to construct e...
A minimum divergence estimation method is developed for robust parameter estimation. The proposed ap...
summary:Point estimators based on minimization of information-theoretic divergences between empirica...
The aim of robust statistics is to develop statistical procedures which are not unduly influenced by...
This paper compares the minimum divergence estimator of Basu et al. (1998) to a competing minimum di...
The aim of robust statistics is to develop statistical procedures which are not unduly influenced by...
A robust estimator introduced by Beran (1977a, 1977b)?Mich i based on the minimum Hellinger distance...
Basu et al. (1998) proposed the minimum divergence estimating method which is free from using the pa...
AbstractIn the present work, the problem of estimating parameters of statistical models for categori...
Here we investigate the robustness properties of the class of minimum power divergence estimators fo...
In this note we introduce some divergence-based model selection criteria. These criteria are defined...
Robust inference based on the minimization of statistical divergences has proved to be a useful alte...