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Expected predictive least squares for model selection in covariance structures

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  • Ogasawara, Haruhiko

Abstract

Predictive least squares (PLS) using future data to be predicted by current data are defined in covariance structure analysis. The expected predictive least squares (EPLS) obtained by two-fold expectation of PLS are unknown fit indexes. Using the asymptotic biases of weighted least squares given by current data for estimation of EPLS in covariance structures, corrected least square criteria derived similarly to the Takeuchi information criterion are shown to be asymptotically unbiased under arbitrary distributions. Simulations for model selection in exploratory factor analysis show improvements over typical current fit indexes as RMSEA and AIC.

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  • Ogasawara, Haruhiko, 2017. "Expected predictive least squares for model selection in covariance structures," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 151-164.
    • Handle: RePEc:eee:jmvana:v:155:y:2017:i:c:p:151-164
    DOI: 10.1016/j.jmva.2016.12.007
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    References listed on IDEAS

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    1. Feinian Chen & Patrick J. Curran & Kenneth A. Bollen & James Kirby & Pamela Paxton, 2008. "An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models," Sociological Methods & Research, , vol. 36(4), pages 462-494, May.
    2. Ke-Hai Yuan & Peter Bentler, 2000. "On equivariance and invariance of standard errors in three exploratory factor models," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 121-133, March.
    3. S. Lee & R. Jennrich, 1979. "A study of algorithms for covariance structure analysis with specific comparisons using factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 44(1), pages 99-113, March.
    4. Karl Jöreskog & Arthur Goldberger, 1972. "Factor analysis by generalized least squares," Psychometrika, Springer;The Psychometric Society, vol. 37(3), pages 243-260, September.
    5. A. Swain, 1975. "A class of factor analysis estimation procedures with common asymptotic sampling properties," Psychometrika, Springer;The Psychometric Society, vol. 40(3), pages 315-335, September.
    6. Ogasawara, Haruhiko, 2009. "Asymptotic expansions in mean and covariance structure analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 902-912, May.
    7. Haruhiko Ogasawara, 2007. "Higher-order approximations to the distributions of fit indexes under fixed alternatives in structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 227-243, June.
    8. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258.
    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.
    10. Kenneth A. Bollen, 1989. "A New Incremental Fit Index for General Structural Equation Models," Sociological Methods & Research, , vol. 17(3), pages 303-316, February.
    11. repec:ucp:bkecon:9780226316529 is not listed on IDEAS
    12. Ogasawara, Haruhiko, 2016. "Bias correction of the Akaike information criterion in factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 144-159.
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    1. Ogasawara, Haruhiko, 2018. "A family of the information criteria using the phi-divergence for categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 87-103.

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