A forward search procedure for identifying influential observations in the estimation of a covariance matrix

Structural Equation Modeling

Volumn 11, Issue 3, 2004, Pages 357-374

  Poon, Wai Yin ^a   Wong, Yuen Kwan ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; LINEAR SYSTEMS; MATRIX ALGEBRA; PROBLEM SOLVING; REGRESSION ANALYSIS; STATISTICAL METHODS; VARIATIONAL TECHNIQUES;

COVARIANCE MODELS; MULTIVARIATE ANALYSIS; STRUCTURAL EQUATION MODELING; SWAMPING EFFECTS;

PARAMETER ESTIMATION;

EID: 3042847012     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1207/s15328007sem1103_4     Document Type: Article

References (29)

1. Anderson, T. W. (1974). An introduction to multivariate statistical analysis. New Delhi: Wiley.
2. Atkinson, A. C. (1994). Fast very robust methods for the detection of multiple outliers. Journal of the American Statistical Association, 89, 1329-1339.
3. Atkinson, A. C., & Mulira, H. M. (1993). The stalactite plot for the detection of multivariate outliers. Statistics and Computing, 3, 27-35.
4. Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics. New York: Wiley.
5. Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.
6. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
7. Bruce, A. G., & Martin, R. D. (1989). Leave-k-out diagnostics for time series. The Journal of the Royal Statistical Society, 51, 363-424.
8. Cermak, G. W., & Bollen, K. A. (1984). Observer consistency in judging extent of cloud cover. Atmospheric Environment, 17, 2107-2110.
9. Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics, 19, 15-18.
10. Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. New York: Chapman & Hall.
11. Hadi, A. S. (1992). Identifying multiple outliers in multivariate data. Journal of the Royal Statistical Society, 54, 761-771.
12. Hadi, A. S., & Simonoff, J. S. (1993). Procedures for the identification of multiple outliers in linear models. Journal of the American Statistical Association, 88, 1264-1272.
13. Hawkins, D. M., Bradu, D., & Kass, G. V. (1984). Location of several outliers in multiple regression data using elemental sets. Technometrics, 26, 197-208.
14. Hawkins, D. M., & Simonoff, J. S. (1993). High breakdown regression and multivariate estimation. Applied Statistics, 42, 423-441.
15. Lawrance, A. J. (1991). Local and deletion influence. In W. Stahel & S. Weisberg (Eds.), Directions in robust statistics and diagnostics, Part I (pp. 141-157). Berlin: Springer.
16. Mardia, K. V. (1970). Measure of multivariate skewness and kurtosis with applications. Biometrika, 57, 519-530.
17. Mardia, K. V. (1974). Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhya, 36, 115-128.
18. Mullen, M. R., Milne, G. R., & Doney, P. M. (1994). An international marketing application of outlier analysis for structural equations: A methodological note. Journal of International Marketing, 3, 45-62.
19. Pena, D., & Yohai, V. J. (1995). The detection of influential subsets in linear regression by using an influence matrix. Journal of the Royal Statistical Society, 57, 145-156.
20. Poon, W. Y., Lew, S. F., & Poon, Y. S. (2000). A local influence approach to identifying multiple multivariate outliers. British Journal of Mathematical and Statistical Psychology, 53, 255-273.
21. Poon, W. Y., & Poon, Y. S. (2002). Influential observations in the estimation of mean vector and covariance matrix. British Journal of Mathematical and Statistical Psychology, 55, 177-192.
22. Rocke, D. M., & Woodruff, D. L. (1996). Identification of outliers in multivariate data. Journal of the American Statistical Association, 91, 1047-1061.
23. Rousseeuw, P. J., & Leroy, A. (1987). Robust regression and outlier detection. New York: Wiley.
24. Rousseeuw, P. J., & van Zomeren, B. C. (1990). Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association, 85, 633-639.
25. Simonoff, J. S. (1991). General approaches to stepwise identification of unusual values in data analysis. In W. Stahel & S. Weisberg (Eds.), Directions in robust statistics and diagnostics. Part III (pp. 223-242). Berlin: Springer.
26. Yuan, K. H., & Bentler, P. M. (1998a). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51, 63-88.
27. Yuan, K. H., & Bentler, P. M. (1998b). Structural equation modeling with robust covariances. Sociological Methodology, 28, 363-396.
28. Yuan, K. H., & Bentler, P. M. (2001). Effect of outliers on estimators and tests in covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 54, 161-175.
29. Yuan, K. H., Chan, W., & Bentler, P. M. (2000). Robust transformation with applications to structural equation modeling. British Journal of Mathematical and Statistical Psychology, 53, 31-50.