Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data

Psychometrika

Volumn 75, Issue 4, 2010, Pages 649-674

  Jamshidian, Mortaza ^a   Jalal, Siavash ^a  

^a CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

covariance structures; k sample test; missing data; multiple imputation; nonparametric test; structural equations; test of homogeneity of covariances

Indexed keywords

EID: 78649502269     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11336-010-9175-3     Document Type: Article

References (25)

1. Anderson, T. W., & Darling, D. A. (1954). A test of goodness of fit. Journal of the American Statistical Association, 49, 765-769.
2. Bentler, P. M., Kim, K. H., & Yuan, K. H. (2004). Testing homogeneity of covariances with infrequent missing data patterns. Unpublished Manuscript.
3. David, F. N. (1939). On Neyman's "smooth" test for goodness of fit: I. Distribution of the criterion Ψ2 when the hypothesis tested is true. Biometrika, 31, 191-199.
4. Fisher, R. A. (1932). Statistical methods for research workers (4th ed.). London: Oliver & Boyd.
5. Hawkins, D. M. (1981). A new test for multivariate normality and homoscedasticity. Technometrics, 23, 105-110.
6. Hou, C. D. (2005). A simple approximation for the distribution of the weighted combination of non-independent or independent probabilities. Statistics and Probability Letters, 73, 179-187.
7. Jamshidian, M., & Bentler, P. M. (1999). ML estimation of mean and covariance structures with missing data using complete data routines. Journal of Educational and Behavioral Statistics, 24, 21-41.
8. Jamshidian, M., & Schott, J. (2007). Testing equality of covariance matrices when data are incomplete. Computational Statistics and Data Analysis, 51, 4227-4239.
9. Kim, K. H., & Bentler, P. M. (2002). Tests of homogeneity of means and covariance matrices for multivariate incomplete data. Psychometrika, 67, 609-624.
10. Kruskal, W., & Wallis, W. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, 583-621.
11. Ledwina, T. (1994). Data-driven version of Neyman's smooth test of fit. Journal of the American Statistical Association, 89, 1000-1005.
12. Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83, 1198-1202.
13. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data (1st edn.). New York: Wiley.
14. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd edn.). New York: Wiley.
15. Marhuenda, Y., Morales, D., & Pardo, M. C. (2005). A comparison of uniformity tests. Statistics, 39, 315-328.
16. Neyman, J. (1937). Smooth test for goodness of fit. Skandinavisk Aktuarietidskrift, 20, 150-199.
17. Rayner, J. C. W., & Best, D. J. (1990). Smooth tests of goodness of fit: An overview. International Statistical Review, 58, 9-17.
18. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592.
19. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.
20. Scholz, F. W., & Stephens, M. A. (1987). K-sample Anderson-Darling tests. Journal of the American Statistical Association, 82, 918-924.
21. Srivastava, M. S. (2002). Methods of multivariate statistics. New York: Wiley.
22. Srivastava, M. S., & Dolatabadi, M. (2009). Multiple imputation and other resampling scheme for imputing missing observations. Journal of Multivariate Analysis, 100, 1919-1937.
23. Thas, O., & Ottoy, J.-P. (2004). An extension of the Anderson-Darling k-sample test to arbitrary sample space partition sizes. Journal of Statistical Computation and Simulation, 74, 651-665.
24. Yuan, K. H. (2009). Normal distribution pseudo ML for missing data: With applications to mean and covariance structure analysis. Journal of Multivariate Analysis, 100, 1900-1918.
25. Yuan, K. H., Bentler, P. M., & Zhang, W. (2005). The effect of skewness and kurtosis on mean and covariance structure analysis: The univariate case and its multivariate implication. Sociological Methods & Research, 34, 240-258.