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journal article Sep 01, 1987

Factor Analysis and AIC

Hirotugu Akaike
Psychometrika Vol. 52 No. 3 pp. 317-332 · Cambridge University Press (CUP)
View at Publisher Save 10.1007/bf02294359
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.
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References
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[2]
Lawley (1971)
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Akaike (1980)
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Akaike (1973)
[12]
Tsumura "On the convergence of iterative procedures in factor analysis" TRU Mathematics (1981)
[16]
Akaike (1985)
[17]
Harman (1960)
[18]
Tsumura "On the unique convergence of iterative procedures in factor analysis" TRU Mathematics (1968)
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Metrics
4,607
Citations
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References
Details
Published
Sep 01, 1987
Vol/Issue
52(3)
Pages
317-332
License
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Authors
H
Hirotugu Akaike
Cite This Article
Hirotugu Akaike (1987). Factor Analysis and AIC. Psychometrika, 52(3), 317-332. https://doi.org/10.1007/bf02294359
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