Abstract
During the last fifteen years, Akaike's entropy-based Information Criterion (AIC) has had a fundamental impact in statistical model evaluation problems. This paper studies the general theory of the AIC procedure and provides its analytical extensions in two ways without violating Akaike's main principles. These extensions make AIC asymptotically consistent and penalize overparameterization more stringently to pick only the simplest of the “true” models. These selection criteria are called CAIC and CAICF. Asymptotic properties of AIC and its extensions are investigated, and empirical performances of these criteria are studied in choosing the correct degree of a polynomial model in two different Monte Carlo experiments under different conditions.
Keywords
Akaike information criterionMathematicsModel selectionEntropy (arrow of time)Applied mathematicsSelection (genetic algorithm)Bayesian information criterionInformation CriteriaStatisticsMonte Carlo methodInformation theoryEconometricsComputer scienceArtificial intelligence
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Publication Info
- Year
- 1987
- Type
- article
- Volume
- 52
- Issue
- 3
- Pages
- 345-370
- Citations
- 4411
- Access
- Closed
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Cite This
Hamparsum Bozdogan
(1987).
Model Selection and Akaike's Information Criterion (AIC): The General Theory and its Analytical Extensions.
Psychometrika
, 52
(3)
, 345-370.
https://doi.org/10.1007/bf02294361
Identifiers
- DOI
- 10.1007/bf02294361