A Weighted Estimating Equation for Missing Covariate Data with Properties Similar to Maximum Likelihood

Journal of the American Statistical Association

Volumn 94, Issue 448, 1999, Pages 1147-1160

  Lipsitz, Stuart R ^a   Ibrahim, Joseph G ^b   Zhao, Lue Ping ^c  

^a MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^b DANA FARBER CANCER INSTITUTE   (United States)

^c Fred Hutchinson Cancer Research Center   (United States)

Author keywords

Generalized linear model; Missing at random; Missing completely at random; Missing data mechanism

Indexed keywords

EID: 0442278074     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.1999.10473870     Document Type: Article

References (17)

1. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977), "Maximum Likelihood From Incomplete Data via the EM Algorithm" (with discussion), Journal of the Royal Statistical Society, Ser. B, 39, 1-38.
2. Falkson, G., Cnaan, A., and Simson, I. W. (1990), A randomized Phase II study of acivicin and 4'Deoxydoxorubicin in patients with hepatocellular carcinoma in an Eastern Cooperative Oncology Group Study. American Journal Of Clinical Oncology, 13. 510-515.
3. Gilks, W. R., and Wild, P. (1992), "Adaptive Rejection Sampling for Gibbs Sampling" Applied Statistics, 41, 337-348.
4. Ibrahim, J. G. (1990) "Incomplete Data in Generalized Linear Models." Journal of the American Statistical Association, 85. 765-769.
5. Ibrahim, J. G., Chen, M. H., and Lipstiz, S. R. (1999) "Monte Carlo EM for Missing Covariates in Parametric Regression Models," Biometrics, 55,591-596.
6. Kalish, L. A. (1992), "Phase III Multiple Myeloma: Evaluation of Combi nation Chemotherapy in Previously Untreated Patients." Technical Report 726E, Dana-Farber Cancer Institute, Dept, of Biostatistics.
7. Lipsitz, S. R. and Ibrahim, J. G. (1996), "A Conditional Model for Incomplete Covariates in Parametric Regression Models," Biometrika, 83. 916-922.
8. Little, R. J. A., and Rubin, D. B. (1987), Statistical Analysis With Missing Data, New York: Wiley.
9. Little, R. J. A., and Schluchter, M. (1985), "Maximum Likelihood Estimation for Mixed Continuous and Categorical Data With Missing Values," Biometrika, 72, 497-512.
10. Ixjuis, T. (1982), "Finding the Observed Information Matrix When Using the EM Algorithm," Journal of the Royal Statistical Society, Ser. B, 44, 226-233.
11. McCullagh, R., and Nelder, J. A. (1989) Generalized Linear Models (2nd ed.). London: Chapman and Hall.
12. Robins, I. M., and Ritov, Y. (1997), 'Toward a Curse of Dimensionality Appropriate (CODA) Asymptotic Theory for Semi-Parametric Models," Statistics in Medicine, 16, 285-319.
13. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994), "Estimation of Re-gression Coefficients When Some Regressors are not Always Observed," Journal of the Royal Statistical Society, 89, 846-866.
14. Rubin, D. B. (1976), "Inference and Missing Data." Biometrika, 63. 581-592.
15. Wei, G. C., and Tanner, M. A. (1990) "A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms," Journal of the American Statistical Association, 85, 699-704.
16. White, H. (1982), "Maximum Likelihood Estimation Under Mis Specified Models," Econometrica, 50, 1-26.
17. Zhao, L. P., Lipsitz, S. R., and Lew, D. (1996), "Regression Analysis With Missing Covariate Data Using Estimating Equations," Biometrics, 52, 1165-1182.