The Akaike information criterion, AIC, and its corrected version, AIC c are two methods for selecting normal linear regression models. Both criteria were designed as estimators of the expected Kullback-Leibler information between the model generating th
This paper deals with correcting a bias of Akaike’s information criterion (AIC) for selecting variab...
The selection of an appropriate model is a fundamental step of the data analysis in small area estim...
<p>Log likelihoods (LL), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)...
The Akaike information criterion, AIC, and its corrected version, AICc are two methods for selecting...
Estimation of the expected Kullback-Leibler information is the basis for deriving the Akaike informa...
Akaike information criteria (AIC) for multivariate regression and multiple linear regression models ...
The Akaike information criterion, AIC, and the Mallows ' Cp criterion have been pro-posed as ap...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...
A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregres...
Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical mod...
The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as ...
<p>Table of Akaike Information Criterion (AIC) and Adjusted R<sup>2</sup> values for the different p...
<p>The Akaike information criterion (AICc) values for regression models investigating the effects of...
In statistical settings such as regression and time series, we can condition on observed informatio...
This paper deals with correcting a bias of Akaike’s information criterion (AIC) for selecting variab...
The selection of an appropriate model is a fundamental step of the data analysis in small area estim...
<p>Log likelihoods (LL), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)...
The Akaike information criterion, AIC, and its corrected version, AICc are two methods for selecting...
Estimation of the expected Kullback-Leibler information is the basis for deriving the Akaike informa...
Akaike information criteria (AIC) for multivariate regression and multiple linear regression models ...
The Akaike information criterion, AIC, and the Mallows ' Cp criterion have been pro-posed as ap...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...
A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregres...
Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical mod...
The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as ...
<p>Table of Akaike Information Criterion (AIC) and Adjusted R<sup>2</sup> values for the different p...
<p>The Akaike information criterion (AICc) values for regression models investigating the effects of...
In statistical settings such as regression and time series, we can condition on observed informatio...
This paper deals with correcting a bias of Akaike’s information criterion (AIC) for selecting variab...
The selection of an appropriate model is a fundamental step of the data analysis in small area estim...
<p>Log likelihoods (LL), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)...
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