Advances in Analysis of Mean and Covariance Structure when Data are Incomplete

Handbook of Latent Variable and Related Models

Volumn , Issue , 2007, Pages 21-44

  Jamshidian, Mortaza ^a   Mata, Matthew ^a  

^a CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84882542033     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-044452044-9/50005-7     Document Type: Chapter

References (61)

1. Allison P.D. Estimation of linear models with incomplete data. Sociological Methodology 1987, 71-103. American Sociological Association, Washington, DC. C. Clogg (Ed.).
2. Allison P.D. Missing data techniques for structural equation modeling. Journal of Abnormal Psychology 2003, 112:545-557.
3. Arbuckle J.L. Full information estimation in the presence of incomplete data. Advanced Structural Equation Modeling 1996, Lawrence Erlbaum, New Jersey. G.E. Marcoulides, R.E. Schumacker (Eds.).
4. Arminger G., Sobel M.E. Pseudo maximum likelihood estimation of mean and covariance structures with missing data. Journal of the American Statistical Association 1990, 85:195-203.
5. Azen S., Van Guilder M. Conclusions regarding algorithms for handling incomplete data. Proceedings of the Statistical Computing Section 1981, 53-56. American Statistical Association.
6. Bentler, P.M., Wu, E.J.C. (In press). EQS for Windows User's Guide.
7. Bernaards C.A., Sijtsma K. Factor analysis of multidimensional polytomous item response data suffering from ignorable item nonresponse. Multivariate Behavioral Research 1999, 34:277-313.
8. Bernaards C.A., Sijtsma K. Influence of imputation and EM methods on factor analysis when item nonresponse in questionnaire data is nonignorable. Multivariate Behavioral Research 2000, 35:321-364.
9. Brown C.H. Asymptotic comparison of missing data procedures for estimating factor loadings. Psychometrika 1983, 48:269-291.
10. Buck S.F. A method for estimation of missing values in multivariate data suitable for use with an electronic computer. Journal of the Royal Statistical Society B 1960, 22:302-306.
11. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society B 1977, 39:1-38.
12. BMDP Statistical Software 1988, University of California Press, Berkeley. W.J. Dixon (Ed.).
13. Efron B. Missing data, imputation, and the bootstrap (with discussion). Journal of the American Statistical Association 1994, 89:463-479.
14. Enders C.K. The impact of non-normality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods 2001, 6:352-370.
15. Enders C.K., Peugh J.L. Using an EM covariance matrix to estimate structural equation models with missing data: Choosing an adjusted sample size to improve the accuracy of inferences. Structural Equations Modeling 2004, 11:1-19.
16. Ferguson T.S. A Course in Large Sample Theory 1996, Chapman and Hall, London.
17. Finkbeiner C. Estimation for the multiple factor model when data are missing. Psychometrika 1979, 44:409-420.
18. Graham J.W. Adding missing-data relevant variables to FIML-based structural equations models. Structural Equations Modeling 2003, 10:80-100.
19. Graham J.W., Donaldson S.I. Evaluating interventions with differential attrition: The importance of non-response mechanism and use of follow-up data. Journal of Applied Psychology 1993, 78:119-128.
20. Jamshidian M. An EM algorithm for ML factor analysis with missing data. Latent Variable Modeling and Applications to Causality 1997, 247-258. Springer-Verlag. M. Berkane (Ed.).
21. Jamshidiam M. On algorithms for restricted maximum likelihood estimation. Computational Statistics and Data Analysis 2004, 45:137-157.
22. Jamshidian M. Strategies for analysis of missing data. Handbook of Data Analysis 2004, 113-130. Sage Publication. M. Hardy, A. Bryman (Eds.).
23. Jamshidian M., Bentler P.M. Using complete data routines for ML estimation of mean and covariance structures with missing data. Journal of Educational and Behavioral Statistics 1999, 24:21-41.
24. Jamshidian M., Jennrich R.I. Standard errors for EM estimation. Journal of the Royal Statistical Society B 2000, 62:257-270.
25. Jamshidian M., Schott J. Testing equality of covariance matrices when data are incomplete. Computational Statistics and Data Analysis 2005, In press.
26. Jöreskog K.G., Sörbom D. LISREL 8.7 [Computer Software] 2004, Scientific Software International, Inc., Licolnwood, IL.
27. Kenward M.G., Molenberghs G. Likelihood based frequentist inference when data are missing at random. Statistical Science 1998, 13:236-247.
28. Kim J.O., Curry J. The treatment of missing data in multivariate analysis. Sociological Methods Research 1977, 6:215-240.
29. Kim K.H., Bentler P.M. Tests of homogeneity of means and covariance matrices for multivariate incomplete data. Psychometrika 2002, 67:609-623.
30. Kogovsek T., Ferligoj A., Coenders G., Saris W.E. Estimating the reliability and validity of personal support measures: full information ML estimation with planned incomplete data. Social Networks 2002, 24:1-20.
31. Krishnamoorthy K., Pannala M.K. Some simple test procedures for normal mean vector with incomplete data. Annals of Institute of Statistical Mathematics 1998, 50:531-542.
32. Laird N.M. Missing data in longitudinal-studies. Statistics in Medicine 1988, 7:305-315.
33. Lee S.Y. Estimation for structural equation models with missing data. Psychometrika 1986, 51:93-99.
34. Lee S.Y., Chiu Y.M. Analysis of multivariate polychoric correlation models with incomplete data. British Journal of Mathematical and Statistical Psychology 1990, 43:145-154.
35. Lee S.-Y., Song X.-Y. Maximum likelihood estimation and model comparison for mixtures of structural equation models with ignorable missing data. Journal of Classification 2003, 20:221-225.
36. Lee S.Y., Tang M.L. Analysis of structural equations models with incomplete polytomous data. Communications in Statistics-Theory and Methods 1992, 21:213-232.
37. Lee S.Y., Tang N.S. Bayesian analysis of nonlinear structural equation models with nonignorable missing outcome data. Psychometrika 2006, In press.
38. Lee S.Y., Zhu H.T. Maximum likelihood estimation of nonlinear structural equation models. Psychometrika 2002, 67:189-210.
39. Lee S.-Y., Song X.-Y., Lee J.C.K. Maximum likelihood estimation of nonlinear structural equation models with ignorable missing data. Journal of Educational and Behavioral Statistics 2003, 28:111-134.
40. Little R.J.A. A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association 1988, 83:1198-1202.
41. Little R.J.A., Rubin D.B. Statistical Analysis with Missing Data 1987, Wiley, New York.
42. Little R.J.A., Rubin D.B. Statistical Analysis with Missing Data 2002, Wiley, New York. second ed.
43. Louis T.A. Finding the observed information matrix when using EM algorithm. Journal of the Royal Statistical Society B 1982, 44:226-233.
44. Marsh H.W. Pairwise deletion for missing data in structural equations model: nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equations Modeling 1998, 5:22-36.
45. McDonald R.P. A general approach to nonlinear factor analysis. Psychometrika 1962, 27:123-157.
46. Meng X.L., Rubin D.B. Maximum likelihood via the ECM algorithm: a general framework. Biometrika 1993, 80:267-278.
47. Muthén L.K., Muthén B.O. Mplus User's Guide 2006, Muthén & Muthén, Los Angeles, CA. fourth ed.
48. Muthén B., Kaplan D., Hollis M. On structural equation modeling with data that are not missing completely at random. Psychometrika 1987, 52:431-462.
49. Rabe-Hesketh S., Skrondal A., Pickles A. Generalized multilevel structural equation modeling. Psychometrika 2004, 69:167-190.
50. Rabe-Hesketh S., Skrondal A., Pickles A. GLLAMM Manual. Working Paper 160. U.C. Berkeley Division of Biostatistics Working paper Series 2004, http://www.bepress.com/ucbbiostat/paper160/.
51. Rubin D.B. Inference and missing data. Biometrika 1976, 63:581-592.
52. Rubin D.B. Multiple Imputation for Nonresponse in Surveys 1987, Wiley, New York.
53. Schafer J.L. Analysis of Incomplete Multivariate Data. Monographs on Statistics and Applied Probability 1997, vol. 72. Chapman and Hall.
54. Schafer J.L., Graham J.W. Missing data: Our view of the state of the art. Psychological Methods 2002, 7:147-177.
55. Song J.W., Belin T.R. Imputation for incomplete high-dimensional multivariate normal data using a common factor model. Statistics in Medicine 2004, 23:2827-2843.
56. Song X.-Y., Lee S.Y. Analysis of structural equation model with ignorable missing continuous and polytomous data. Psychometrika 2002, 67:261-288.
57. Tang M., Bentler P.M. Theory and methods for constrained estimation in structural equation models with incomplete data. Computational Statistics and Data Analysis 1998, 27:257-270.
58. Wei G.C.G., Tanner M.A. A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Journal of the American Statistical Association 1990, 82:805-811.
59. Woodbury M.A., Siler W. Factor analysis with missing data. Annals of the New York Academy of Sciences 1966, 128(A3):746.
60. Yuan K.-H., Bentler P.M. Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology 2000, 30:167-202.
61. Yuan K.-H., Lambert P.L., Fouladi R.T. Mardia's multivariate kurtosis with missing data. Multivariate Behavioral Research 2004, 39:413-437.