Factor Analysis with EM Algorithm Never Gives Improper Solutions when Sample Covariance and Initial Parameter Matrices Are Proper

Author:

Adachi Kohei

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,General Psychology

Reference23 articles.

1. Anderson, J.C., & Gerbing, D.W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155–173.

2. Anderson, T.W., & Rubin, H. (1956). Statistical inference in factor analysis. In J. Neyman (Ed.), Proceedings of the third Berkeley symposium on mathematical statistics and probability (pp. 111–150). Berkeley: University of California Press.

3. Dempster, A.P., Laird, N.M., & Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B, 39, 1–38.

4. Gerbing, D.W., & Anderson, J.C. (1987). Improper solutions in the analysis of covariance structures: their interpretability and a comparison of alternative respecifications. Psychometrika, 52, 99–111.

5. Jennrich, R.I., & Robinson, S.M. (1969). A Newton–Raphson algorithm for maximum likelihood factor analysis. Psychometrika, 34, 111–123.

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