, Volume 52, Issue 3, pp 317332
Factor analysis and AIC
 Hirotugu AkaikeAffiliated withThe Institute of Statistical Mathematics
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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.
Key words
factor analysis maximum likelihood information criterion AIC improper solution Bayesian modeling Title
 Factor analysis and AIC
 Journal

Psychometrika
Volume 52, Issue 3 , pp 317332
 Cover Date
 198709
 DOI
 10.1007/BF02294359
 Print ISSN
 00333123
 Online ISSN
 18600980
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 factor analysis
 maximum likelihood
 information criterion AIC
 improper solution
 Bayesian modeling
 Industry Sectors
 Authors

 Hirotugu Akaike ^{(1)}
 Author Affiliations

 1. The Institute of Statistical Mathematics, 467 MinamiAzabu, MinatoKu, 106, Tokyo, Japan