Add to ORKGVariable selection in the presence of outliers may be performed by using a robust version of Akaike's information criterion AIC. In this paper explicit expressions are obtained for such criteria when S and MM-estimators are used. The performance of these criteria is compared to the existing AIC based on M-estimators and to the classical non-robust AIC. In a simulation study and in data examples we observe that the proposed AIC with S and MM-estimators selects more appropriate models in case outliers are present.status: publishe
In linear mixed models, the conditional Akaike Information Criterion (cAIC) is a procedure for varia...
This study looks at two problems related to the robust variable selection in linear regression mode...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...
Variable selection in the presence of outliers may be performed by using a robust version of Akaike'...
Variable selection in the presence of outliers may be performed by using a robust version of Akaike'...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
Estimation of the expected Kullback-Leibler information is the basis for deriving the Akaike informa...
Robust model selection procedures are introduced as a robust modification of the Akaike information ...
Model selection is a key component in any statistical analysis. In this paper we discuss this issue ...
We study estimation and model selection on both the fixed and the random effects in the setting of l...
The various debates around model selection paradigms are important, but in lieu of a consensus, ther...
Akaike Information Criterion (AIC) has been used widely as a statistical criterion to compare the ap...
The Akaike information criterion (AIC) has been successfully used in the liter-ature in model select...
The selection of an appropriate model is a fundamental step of the data analysis in small area estim...
In linear mixed models, the conditional Akaike Information Criterion (cAIC) is a procedure for varia...
This study looks at two problems related to the robust variable selection in linear regression mode...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...
Variable selection in the presence of outliers may be performed by using a robust version of Akaike'...
Variable selection in the presence of outliers may be performed by using a robust version of Akaike'...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
Estimation of the expected Kullback-Leibler information is the basis for deriving the Akaike informa...
Robust model selection procedures are introduced as a robust modification of the Akaike information ...
Model selection is a key component in any statistical analysis. In this paper we discuss this issue ...
We study estimation and model selection on both the fixed and the random effects in the setting of l...
The various debates around model selection paradigms are important, but in lieu of a consensus, ther...
Akaike Information Criterion (AIC) has been used widely as a statistical criterion to compare the ap...
The Akaike information criterion (AIC) has been successfully used in the liter-ature in model select...
The selection of an appropriate model is a fundamental step of the data analysis in small area estim...
In linear mixed models, the conditional Akaike Information Criterion (cAIC) is a procedure for varia...
This study looks at two problems related to the robust variable selection in linear regression mode...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...