Principal component analysis using LISREL 8

Structural Equation Modeling

Volumn 3, Issue 4, 1996, Pages 307-322

  Dolan, Conor ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0010000127     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705519609540049     Document Type: Article

References (25)

1. Aitchison, J., & Silvey, S. D. (1958). Maximum-likelihood estimation of parameters subject to restraints. Annals of Mathematical Statistics, 29, 813-828.
2. Basilevsky, A. (1983). Applied matrix algebra in the statistical sciences. New York: Elsevier Science.
3. Browne, M. W., & du Toit, S. H. C. (1992). Automated fitting of non standard models. Multivariate Behavioral Research, 27, 269-300.
4. Clarkson, D. B. (1988). A remark on algorithm AS 211, the F-G diagonalization algorithm. Applied Statistics, 25, 147-151.
5. Flury, B. (1988). Common principal components and related multivariate models. New York: Wiley.
6. Flury, B., & Constantine, G. (1985). Algorithm AS 211, the F-G diagonalization algorithm. Applied Statistics, 34, 117-183.
7. Flury, B. D., & Neuenschwander, B. E. (1995). Principal component models for patterned covariance matrices with applications to canonical correlation analysis of several sets of variables. In W. J. Krzanowski (Ed.), Recent advances in descriptive multivariate analysis (pp. 90-112). Oxford, England: Clarendon.
8. Greene, W. H. (1993). Econometric analysis (2nd ed.). New York: MacMillan.
9. Greybill, F. A. (1983). Matrices with applications in statistics (2nd ed.). Belmont, CA: Wadsworth.
10. IMSL. (1991). STAT/LIBRARY (Version 2). Houston, TX: Author.
11. Jolicoeur, P. (1984). Principal components, factor analysis, and multivariate allometry: A small-sample detection test. Biometrics, 40, 685-690.
12. Joliffe, I. T. (1986). Principal component analysis. New York: Springer-Verlag.
13. Jöreskog, K. G., & Sörbom, D. (1988). LISREL 7: A guide to the program and applications. Chicago: SPSS.
14. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modelling with the SIMPLIS command language. Chicago: Scientific Software International.
15. Krzanowski, W. J. (1984). Sensitivity of principal components. Journal of the Royal Statistical Society, B, 46, 558-563.
16. Lawley, D. N., & Maxwell, A. E. (1971). Factor analysis as a statistical method. London: Butterworths.
17. Morrison, D. F. (1990). Multivariate statistical methods. New York: McGraw-Hill.
18. Neale, M. C. (1994). Mx: Statistical modeling (2nd ed.). Richmond, VA: Medical College of Virginia, Virginia Commonwealth University, Department of Psychiatry.
19. Price, B. (1977). Ridge regression: Application to nonexperimental data. Psychological Bulletin, 84, 759-766.
20. Reyment, R., & Jöreskog, K. G. (1993). Applied factor analysis in the natural sciences. Cambridge, England: Cambridge University Press.
21. Sinha, A. R., & Buchanan, B. S. (1995). Assessing the stability of principal components using regression. Psychometrika, 60, 355-369.
22. Sparks, D. N., & Todd, A. D. (1973). Latent roots and vectors of a symmetric matrix. Algorithm AS60. Applied Statistics, 22, 260-265.
23. Stevens, J. (1992). Applied multivariate statistics for the social sciences. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
24. Takemura, A. (1985). A principal decomposition of Hotelling's T2 statistic. In P. R. Krishnaiah (Ed.), Multivariate Analysis-VI (pp. 583-597). Amsterdam: Elsevier Science.
25. Waternaux, C. M. (1976). Asymptotic distribution of sample roots for a nonnormal population. Biometrika, 63, 639-645.