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Bias correction of the Akaike information criterion in factor analysis

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  • Ogasawara, Haruhiko

Abstract

The higher-order asymptotic bias for the Akaike information criterion (AIC) in factor analysis or covariance structure analysis is obtained when the parameter estimators are given by the Wishart maximum likelihood. Since the formula of the exact higher-order bias is complicated, simple approximations which do not include unknown parameter values are obtained. Numerical examples with simulations show that the approximations are reasonably similar to their corresponding exact asymptotic values and simulated values. Simulations for model selection give consistently improved results by the approximate correction of the higher-order bias for the AIC over the usual AIC.

Suggested Citation

  • Ogasawara, Haruhiko, 2016. "Bias correction of the Akaike information criterion in factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 144-159.
  • Handle: RePEc:eee:jmvana:v:149:y:2016:i:c:p:144-159
    DOI: 10.1016/j.jmva.2016.04.003
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    References listed on IDEAS

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    9. Ogasawara, Haruhiko, 2007. "Higher-order Estimation Error in Structural Equation Modeling," 商学討究 (Shogaku Tokyu), Otaru University of Commerce, vol. 57(4), pages 131-160.
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    17. Ogasawara, Haruhiko, 2015. "Asymptotic cumulants of some information criteria," ビジネス創造センターディスカッション・ペーパー (Discussion papers of the Center for Business Creation) 10252/5446, Otaru University of Commerce.
    18. Ogasawara, Haruhiko, 2015. "Asymptotic cumulants of some information criteria (2nd version)," ビジネス創造センターディスカッション・ペーパー (Discussion papers of the Center for Business Creation) 10252/5497, Otaru University of Commerce.
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    1. repec:eee:csdana:v:124:y:2018:i:c:p:87-103 is not listed on IDEAS
    2. Ogasawara, Haruhiko, 2017. "Expected predictive least squares for model selection in covariance structures," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 151-164.

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