Analysis of structural equation models with censored or truncated data via EM algorithm

Computational Statistics and Data Analysis

Volumn 27, Issue 1, 1998, Pages 33-46

  Tang, Man Lai ^a   Lee, Sik Yum ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Censored data; Generalized censoring mechanism (GCM); Generalized EM algorithm; Maximum likelihood estimation; Non ignorable missing mechanism; Structural equation models; Truncated data

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA REDUCTION; DATA STRUCTURES; MATHEMATICAL MODELS; PROBLEM SOLVING;

GENERALIZED CENSORING MECHANISMS (GCM); MAXIMUM LIKELIHOOD ESTIMATION; NON-IGNORABLE MISSING MECHANISMS; STRUCTURAL EQUATION MODELS; TRUNCATED DATA;

STATISTICAL METHODS;

EID: 0032489248     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9473(97)80040-0     Document Type: Article

References (21)

1. Arminger, G., Sobel, M.E., 1990. Pseudo-maximum likelihood estimation of mean and covariance structure with missing data. J. Amer. Statist. Assoc. 85, 195-203.
2. Bentler, P.M., 1983. Simultaneous equations as moment structure models: With an introduction to latent variable models. J. Econometrics 22, 13-42.
3. Bentler, P.M., Wu, E.J.C., 1993. EQS/Windows User's Guide, Version 4. BMDP Statistical Software Inc., Los Angeles.
4. Brown, C.H., 1983. Asymptotic comparison of missing data procedures for estimating factor loadings. Psychometrika 48, 269-291.
5. Brown, C.H., 1990. Protecting against nonrandomly missing data in longitudinal studies. Biometrics 46, 143-155.
6. Brown, R.L., 1994. Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods. Struct. Equat. Modeling 1, 287-316.
7. Byrne, B.M., 1994. Structural equation modeling with EQS and EQS/Windows: Basic concepts, applications, and programming. Sage, Thousand Oaks, CA.
8. Dempster, A.P., Laird, N.M., Rubin, D.R., 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Roy Statist. Soc. Ser. B 39, 1-22.
9. Finkbeiner, C., 1979. Estimation for the multiple factor model when data are missing. Psychometrika 44, 409-420.
10. Joreskog, K.G., 1978. Structural analysis of covariance and correlation matrices Psychometrika 43, 443-477.
11. Joreskog, K.G., Sorbom, D., 1993. LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Scientific Software International, Hove and London.
12. Lee, S.-Y., 1986. Estimation for structural equation models with missing data. Psychometrika 51, 93-99.
13. Lee, S.Y., Poon, W.Y., Bentler, P.M., 1992. Some empirical investigations of LISCOMP. ASA Proc. Statist. Comput. 183-188.
14. Little, R.J.A., Rubin, D.B., 1987. Statistical Analysis with Missing Data. Wiley, New York.
15. Muthen, B., 1989. Tobit factor analysis. British Journal of Mathematical and Statistical Psychology 42, 241-250.
16. Poon, W.Y., Lee, S.Y., Tang, M.L., Analysis of structural equation models with censored data. British Journal of Mathematical and Statistical Psychology (1997a, accepted).
17. Poon, W.Y., Tang, M.L., Lee, S.Y., 1997b. Analysis of covariance structure analysis with truncated variables. Behaviormetrika 24, 39-50.
18. Ratani, R.T., Gajjar, A.V., 1977. Estimation of parameters of a truncated multivariate normal distribution. South African Statist. J. 11, 125-137.
19. Rovine, M.J., 1994. Latent variable models and missing data analysis. In: A. von Eye, C.C. Clogg (Eds.), Latent Variables Analysis: Applications for Developmental Research. Sage, Thousand Oaks, CA, pp. 181-225.
20. Talli, G.M., 1961. The moment generating function of the truncated multinormal distribution. J. Roy. Statist. Soc. Ser. B 23, 223-229.
21. Tang, M.L., Bentler, P.M., 1997. Maximum likelihood estimation in covariance structure analysis with truncated data. British J. Math. Statist. Psychol., accepted.