Assessing local influence for nonlinear structural equation models with ignorable missing data

Computational Statistics and Data Analysis

Volumn 50, Issue 5, 2006, Pages 1356-1377

  Lee, Sik Yum ^a   Lu, Bin ^a   Song, Xin Yuan ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Conformal normal curvature; Gibbs sampler; Local influence; MCEM algorithm

Indexed keywords

COMPUTER SIMULATION; DATA ACQUISITION; MATHEMATICAL MODELS; PERTURBATION TECHNIQUES; RANDOM PROCESSES; STATISTICAL METHODS;

CONFORMAL NORMAL CURVATURE; GIBBS SAMPLER; LOCAL INFLUENCE; MCEM ALGORITHM; MISSING DATA; NONLINEAR STRUCTURAL EQUATIONS;

NONLINEAR EQUATIONS;

EID: 27644527138     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2004.11.012     Document Type: Article

References (34)

1. R.P. Bagozzi, H. Baumgartner, and Y. Yi State versus action orientation and the theory of reasoned action an application to coupon usage J. Consumer Res. 18 1992 505 517
2. R.J. Beckman, and R.D. Cook Outliers Technometrics 25 1983 119 149
3. D.R. Cook Assessment of local influence (with discussion) J. Roy. Statist. Soc. Ser. B 48 1986 133 169
4. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via EM algorithm (with discussion) J. Roy. Statist. Soc. Ser. B 39 1977 1 38
5. S. Geman, and D. Geman Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images IEEE Trans. Pattern Anal. Machine Intell. 6 1984 721 741
6. W.K. Hastings Monte Carlo sampling methods using Markov chains and their application Biometrika 57 1970 97 109
7. J. Jaccard, and C.K. Wan Measurement error in the analysis of interaction effects between continuous predictors using multiple regression multiple indicator and structural equation approaches Psychol. Bull. 117 1995 348 357
8. K.G. Jöreskog, and D. Sörbom LISREL 8 User's Reference Guide 1996 Scientific Software International Hove and London
9. D.A. Kenny, and C.M. Judd Estimating the nonlinear and interactive effects of latent variables Psychol. Bull. 96 1984 201 210
10. C.W. Kwan, and W.K. Fung Assessing local influence for specific restricted likelihood application to factor analysis Psychometrika 63 1998 35 46
11. S.Y. Lee, and X.Y. Song Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables Comput. Statist. Data Anal. 44 2003 125 142
12. S.Y. Lee, and S.J. Wang Sensitivity analysis of structural equation models Psychometrika 61 1996 93 108
13. S.Y. Lee, and L. Xu Influence analysis of nonlinear mixed-effects models Comput. Statist. Data Anal. 45 2004 321 341
14. S.Y. Lee, and H.T. Zhu Maximum likelihood estimation of nonlinear structural equation models Psychometrika 67 2002 189 210
15. S.Y. Lee, X.Y. Song, and C.K. Lee Maximum likelihood estimation of nonlinear structural equation models with ignorable missing data J. Educat. Behav. Statist. 28 2003 111 134
16. E. Lesaffre, and G. Verbeke Local influence in linear mixed models Biometrics 54 1998 570 582
17. R.J.A. Little, and D.B. Rubin Statistical Analysis with Missing Data 1987 Wiley New York
18. X.L. Meng, and D.B. Rubin Maximum likelihood estimation via ECM algorithm a general framework Biometrika 80 1993 267 278
19. X.L. Meng, and S. Schilling Fitting full-information item factor models and an empirical investigation of bridge sampling J. Amer. Statist. Assoc. 91 1996 1254 1267
20. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller Equations of state calculations by fast computing machine J. Chem. Phys. 21 1953 1087 1091
21. J.X. Pan, K.T. Fang, and E.P. Liski Bayesian local influence for the growth curve model with Rao's simple covariance structure J. Multivariate Anal. 58 1996 55 81
22. W.Y. Poon, and Y.S. Poon Conformal normal curvature and assessment of local influence J. Roy. Statist. Soc. Ser. B 61 1999 51 61
23. W.Y. Poon, S.J. Wang, and S.Y. Lee Influence analysis of structural equation models with polytomous variables Psychometrika 64 1999 461 473
24. Schumacker, R.E., Marcoulides, G.A. (Eds.), 1998. Interaction and nonlinear effects in structural equation models. Lawrence Erlbaum Associates, Publishers, Mahwah, NJ.
25. L. Shi Local influence in principal components analysis Biometrika 84 1997 175 186
26. R.T. St. Laurent, and R.D. Cook Leverage, local influence and curvature in nonlinear regression Biometrika 80 1993 99 106
27. Y. Tanaka, and F. Zhang R-mode and Q-mode influence analyses in statistical modelling relationship between influence function approach and local influence approach Comput. Statist. Data Anal. 32 1999 197 218
28. Y. Tanaka, S. Watadani, and S.H. Moon Influence in covariance structure analysis with an application to confirmatory factor analysis Commun. Statist. Ser. A 20 1991 3805 3821
29. M.A. Tanner, and W.H. Wong The calculation of posterior distributions by data augmentation (with discussion) J. Amer. Statist. Assoc. 82 1987 528 550
30. W. Thomas, and R.D. Cook Assessing influence on regression coefficients in generalized linear models Biometrika 76 1989 741 749
31. W. Thomas, and R.D. Cook Assessing influence on predictions from generalized linear models Technometrics 32 1990 59 65
32. S.J. Wang, and S.Y. Lee Sensitivity analysis of structural equation models with equality functional constraints Comput. Statist. Data Anal. 23 1996 239 256
33. G.C.G. Wei, and M.A. Tanner A Monte Carlo implementation of the EM algorithm and the Poor man's data augmentation algorithm J. Amer. Statist. Assoc. 85 1990 699 704
34. H.T. Zhu, and S.Y. Lee Local influence for incomplete data models J. Roy. Statist. Soc. Ser. B 63 2001 111 126