Abstract
The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the order determination of an autoregressive model to the determination of the number of factors in the maximum likelihood factor analysis. The use of the AIC criterion in the factor analysis is particularly interesting when it is viewed as the choice of a Bayesian model. This observation shows that the area of application of AIC can be much wider than the conventional i.i.d. type models on which the original derivation of the criterion was based. The observation of the Bayesian structure of the factor analysis model leads us to the handling of the problem of improper solution by introducing a natural prior distribution of factor loadings.
Keywords
Bayesian information criterionMathematicsStatisticsBayesian probabilityMaximum likelihoodAkaike information criterionAutoregressive modelInformation CriteriaEconometricsFactor (programming language)Marginal likelihoodDeviance information criterionFactor analysisApplied mathematicsBayesian inferenceModel selectionComputer science
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Publication Info
- Year
- 1987
- Type
- article
- Volume
- 52
- Issue
- 3
- Pages
- 317-332
- Citations
- 4988
- Access
- Closed
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Cite This
Hirotugu Akaike
(1987).
Factor Analysis and AIC.
Psychometrika
, 52
(3)
, 317-332.
https://doi.org/10.1007/bf02294359
Identifiers
- DOI
- 10.1007/bf02294359