The accuracy of AIC and BIC is evaluated under simulated multiple regression conditions, varying number of total and valid predictors, R2, and n. AIC and BIC were increasingly accurate as n increased and as total predictors decreased. Interactions of the ratio of valid/total predictors affected accuracy
Prepared under National Science Foundation grant SOC 76-22232.Includes bibliographical references (l...
<p>Akaike information criterion (AIC) values are compared between several sub-models of the RDM. Eac...
We test two questions: (i) Is the Bayesian Information Criterion (BIC) more parsimonious than Akaike...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...
A traditional approach to statistical inference is to identify the true or best model first with lit...
<p>Models selected by various statistical methods. Columns are individual response variables. All mo...
This brief note compares model selection procedures in regression. On the one hand there is an obser...
A traditional approach to statistical inference is to identify the true or best model first with lit...
In model selection, it is necessary to select a model from a set of candidate models based on some o...
In Bioinformatics and other areas the model selection is a process of choosing a model from set of c...
Stock & Watson (1999) consider the relative quality of different univariate forecasting techniques. ...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
<p>Bayesian information criterion (BIC) values are compared between several sub-models of the RDM. E...
The Bayesian information criterion (BIC), the Akaike information criterion (AIC), and some other ind...
This paper is concerned with the model selection and model averaging problems in system identificat...
Prepared under National Science Foundation grant SOC 76-22232.Includes bibliographical references (l...
<p>Akaike information criterion (AIC) values are compared between several sub-models of the RDM. Eac...
We test two questions: (i) Is the Bayesian Information Criterion (BIC) more parsimonious than Akaike...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...
A traditional approach to statistical inference is to identify the true or best model first with lit...
<p>Models selected by various statistical methods. Columns are individual response variables. All mo...
This brief note compares model selection procedures in regression. On the one hand there is an obser...
A traditional approach to statistical inference is to identify the true or best model first with lit...
In model selection, it is necessary to select a model from a set of candidate models based on some o...
In Bioinformatics and other areas the model selection is a process of choosing a model from set of c...
Stock & Watson (1999) consider the relative quality of different univariate forecasting techniques. ...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
<p>Bayesian information criterion (BIC) values are compared between several sub-models of the RDM. E...
The Bayesian information criterion (BIC), the Akaike information criterion (AIC), and some other ind...
This paper is concerned with the model selection and model averaging problems in system identificat...
Prepared under National Science Foundation grant SOC 76-22232.Includes bibliographical references (l...
<p>Akaike information criterion (AIC) values are compared between several sub-models of the RDM. Eac...
We test two questions: (i) Is the Bayesian Information Criterion (BIC) more parsimonious than Akaike...
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