Akaike information criterion

Known as: AICc, Akaike's Information Criterion, AIC 
The Akaike information criterion (AIC) is a measure of the relative quality of statistical models for a given set of data. Given a collection of… (More)
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Topic mentions per year

Topic mentions per year

1975-2017
010020019752017

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2012
2012
In this study, a model identification instrument to determine the variance component structure for generalized linear mixed… (More)
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Review
2011
Review
2011
As use of Akaike’s Information Criterion (AIC) for model selection has become increasingly common, so has a mistake involving… (More)
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Highly Cited
2010
Highly Cited
2010
Akaike’s information criterion (AIC) is increasingly being used in analyses in the field of ecology. This measure allows one to… (More)
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2009
2009
Using an innovations state space approach, it has been found that the Akaike information criterion (AIC) works slightly better… (More)
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2008
2008
Ensemble neural networks are commonly used networks in many engineering applications due to its better generalization property… (More)
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Highly Cited
2007
Highly Cited
2007
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org… (More)
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2007
2007
We examine the problem of jointly selecting the number of components and variables in finite mixture regression models. We find… (More)
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2006
2006
Following the work of Hurvich, Shumway, and Tsai (1990), we propose an “improved” variant of the Akaike information criterion… (More)
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2006
2006
An instance crucial to most problems in signal processing is the selection of the order of a candidate model. Among the different… (More)
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Highly Cited
2000
Highly Cited
2000
  • Bozdogan
  • Journal of mathematical psychology
  • 2000
In this paper we briefly study the basic idea of Akaike's (1973) information criterion (AIC). Then, we present some recent… (More)
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