Add to ORKGAbstract: The aim of this work is to develop a new model selection criterion using a general discrepancy based technique, by constructing an asymptotically unbiased estimator of the overall average discrepancy between the true and the fitted models. Furthermore, the lower bound for the mean squared error of prediction is established
This paper develops two weighted measures for model selection by generalizing the Kullback-Leibler d...
When it is acknowledged that all candidate parameterised statistical models are misspecified relativ...
When it is acknowledged that all candidate parameterised statistical models are misspecified relativ...
In this note we introduce some divergence-based model selection criteria. These criteria are defined...
Version 17/04/2008 This article compares traditional Model Selection Criteria with the recently prop...
In this paper, we propose a new criterion for selection between nested models. We suppose that the c...
In model selection problems, robustness is one important feature for selecting an adequate model fro...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
AbstractThe fundamentals of information theory and also their applications to testing statistical hy...
In this Master Thesis, we have analytically derived and numerically implemented three estimators of ...
The problems of estimating parameters of statistical models for categorical data, and testing hypoth...
Although robust divergence, such as density power divergence and γ-divergence, is helpful for robust...
International audienceRecently, Azari et al (2006) showed that (AIC) criterion and its corrected ver...
AbstractThe problems of estimating parameters of statistical models for categorical data, and testin...
Model selection is of fundamental importance to high dimensional modelling featured in many contempo...
This paper develops two weighted measures for model selection by generalizing the Kullback-Leibler d...
When it is acknowledged that all candidate parameterised statistical models are misspecified relativ...
When it is acknowledged that all candidate parameterised statistical models are misspecified relativ...
In this note we introduce some divergence-based model selection criteria. These criteria are defined...
Version 17/04/2008 This article compares traditional Model Selection Criteria with the recently prop...
In this paper, we propose a new criterion for selection between nested models. We suppose that the c...
In model selection problems, robustness is one important feature for selecting an adequate model fro...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
AbstractThe fundamentals of information theory and also their applications to testing statistical hy...
In this Master Thesis, we have analytically derived and numerically implemented three estimators of ...
The problems of estimating parameters of statistical models for categorical data, and testing hypoth...
Although robust divergence, such as density power divergence and γ-divergence, is helpful for robust...
International audienceRecently, Azari et al (2006) showed that (AIC) criterion and its corrected ver...
AbstractThe problems of estimating parameters of statistical models for categorical data, and testin...
Model selection is of fundamental importance to high dimensional modelling featured in many contempo...
This paper develops two weighted measures for model selection by generalizing the Kullback-Leibler d...
When it is acknowledged that all candidate parameterised statistical models are misspecified relativ...
When it is acknowledged that all candidate parameterised statistical models are misspecified relativ...