Multiple-imputation inferences with uncongenial sources of input

Statistical Science

Volumn 9, Issue 4, 1994, Pages 538-558

  Meng, Xiao Li ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Congeniality; Importance sampling; Incomplete data; Missing data; Nonresponse; Normalizing constants; Public use data file; Randomization; Self efficiency

Indexed keywords

EID: 84972537494     PISSN: 08834237     EISSN: None     Source Type: Journal    
DOI: 10.1214/ss/1177010269     Document Type: Article

References (58)

1. Belin, T. R., Diffendal, G. J., Mack, S., Rubin, D. B., Schafer, J. L. and Zaslavsky, A. M. (1993). Hierarchical logistic regression models for imputation of unresolved enumeration status in undercount estimation (with discussion). J. Amer. Statist. Assoc. 88 1149-1166.
2. Bellhouse, D. R. (1988). A brief history of random sampling methods. In Handbook of Statistics 6-Sampling (P. R. Krishnaiah and C. R. Rao eds.) 1-14. North-Holland, Amsterdam.
3. Bowley, A. L. (1906). Address to the Economic and Statistics Section of the British Association for the Advancement of Science, York, 1906. J. Roy. Statist. Soc. 69 540-558.
4. Clogg, C. C., Rubin, D. B., Schenker, N., Schultz, B. and Weidman, L. (1991). Multiple imputation of industry and occupation codes in census public use samples using Bayes logistic regression. J. Amer. Statist. Assoc. 86 68-78.
5. Dorey, F. J., Little, R. J. A. and Schenker, N. (1990). Multiple imputation for interval-censored threshold data. Paper presented at the 1990 Joint Statistical Meetings, Anaheim.
6. Efron, B. (1994). Missing data, imputation, and the bootstrap (with discussion). J. Amer. Statist. Assoc. 89 463-479.
7. Ericson, W. A. (1969). Subjective Bayesian models in sampling finite populations (with discussion). J. Roy. Statist. Soc. Ser. B 31 195-233.
8. Fay, R. E. (1991). A design-based perspective on missing data variance. In Proceedings of the 1991 Annual Research Conference 429-440. U.S. Bureau of the Census, Washington, D.C.
9. Fay, R. E. (1992). When are inferences from multiple imputation valid? In Proceedings of the Survey Research Methods Section 227-232. Amer. Statist. Assoc., Alexandria, VA.
10. Heitjan, D. F. and Landis, J. R. (1994). Assessing secular trends in blood pressure: a multiple-imputation approach. J. Amer. Statist. Assoc. 89 750-759.
11. Heitjan, D. F. and Little, R. J. A. (1991). Multiple imputation for the fatal accident reporting system. J. Roy. Statist. Soc. Ser. C 40 13-29.
12. Heitjan, D. F. and Rubin, D. B. (1990). Inference from coarse data via multiple imputation with application to age heaping. J. Amer. Statist. Assoc. 85 304-314.
13. Kiaer, A. N. (1895/1896). Observations et expériences concernant des dénombrements représentatifs. Bulletin of the International Statistical Institute 9 176-183.
14. Kott, P. S. (1992). A note on a counter-example to variance estimation using multiple imputation. Technical report, National Agricultural Statistics Service, Washington, D.C.
15. Kruskal, W. and Mosteller, F. (1980). Representative sampling, IV: the history of the concept in statistics, 1895-1939. Internat. Statist. Rev. 48 169-195.
16. Laplace, P. S. (1814). Essai Philosophique sur les Probabilités. Courcier, Paris. [Sixth ed. (1840) translated as A Philosophical Essay on Probabilities, in 1902; reprinted (1951) by Dover, New York.]
17. Lehmann, E. L. (1983). Theory of Point Estimation. Wiley, New York.
18. Li, K. H., Meng, X. L., Raghunathan, T. E. and Rubin, D. B. (1991). Significance levels from repeated p-values with multiply-imputed data. Statist. Sinica 1 65-92.
19. Li, K. H., Raghunathan, T. E. and Rubin,.D. B. (1991). Large sample significance levels from multiply-imputed data using moment-based statistics and an F reference distribution. J. Amer. Statist. Assoc. 86 1065-1073.
20. Little, R. J. A. and Rubin, D. B. (1987). Statistical Analysis with Missing Data. Wiley, New York.
21. Meng, X. L. (1988). Significance levels from the repeated significance levels in multiple imputation. Ph.D. qualifying paper, Dept. Statistics, Harvard Univ.
22. Meng, X. L. (1990). Towards complete results for some incomplete-data problems. Ph.D. dissertation, Dept. Statistics, Harvard Univ. (Printed by U-M-I, Ann Arbor, MI.)
23. Meng, X. L. (1993). Coherent multiple-imputation inference under incoherent models. Technical Report 359, Dept. Statistics, Univ. Chicago.
24. Meng, X. L. and Rubin, D. B. (1992). Performing likelihood ratio tests with multiply-imputed data sets. Biometrika 79 103-109
25. Meng, X. L. and Wong, W. H. (1993). Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Technical Report 365, Dept. Statistics, Univ. Chicago. Statist. Sinica. To appear.
26. Morgenthaler, S. and Tukey, J. W., eds. (1991). Configurai Polysampling. Wiley, New York.
27. Neyman, J. (1934). On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection (with discussion). J. Roy. Statist. Soc. Ser. A 97 558-625.
28. Pratt, J. W. (1965). Bayesian interpretation of standard inference statements. J. Roy. Statist. Soc. Ser. B 27 169-203.
29. Raghunathan, T. E. (1987). Large sample significance levels from multiply-imputed data. Ph.D. dissertation, Dept. Statistics, Harvard Univ.
30. Raghunathan, T. E. (1993). Analysis of case-control study with missing covariates. Technical report, Dept. Biostatistics, Univ. Washington, Seattle.
31. Rao, J. N. K. and Shao, J.(1992). Jackknife variance estimation with survey data under hot deck imputation. Biometrika 79 811-822.
32. Rubin, D. B. (1976). Inference and missing data. Biometrika 63 581-592.
33. Rubin, D. B. (1978). Multiple imputations in sample surveys: a phenomenological Bayesian approach to nonresponse. In Proceedings of the Survey Research Methods Section 20-34. Amer. Statist. Assoc., Alexandria, VA.
34. Rubin, D. B. (1980). Handling nonresponse in sample surveys by multiple imputation. U.S. Bureau of the Census Monograph, Washington, D.C.
35. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Survey s. Wiley, New York.
36. Rubin, D. B. (1995). Multiple imputation after 18 years. J. Amer. Statist. Assoc. To appear.
37. Rubin, D. B. and Schafer, J. L. (1990). Efficiently creating multiple imputations for incomplete multivariate normal data. In Proceedings of the Statistical Computing Section 83-88. Amer. Statist. Assoc., Alexandria, VA.
38. Rubin, D. B., Schafer, J. L. and Schenker, N. (1988). Imputation strategies for missing values in post-enumeration surveys. Survey Methodology 14 209-221.
39. Rubin, D. B. and Schenker, N. (1986). Multiple imputations for interval estimation from simple random samples with ignorable nonresponse. J. Amer. Statist. Assoc. 81 366-374.
40. Rubin, D. B. and Schenker, N. (1987). Interval estimation from multiply-imputed data: a case study using census agriculture industry codes. Journal of Official Statistics 3 375-387.
41. Rubin, D. B. and Zaslavsky, A. M. (1989). An overview of representing within-household and whole-household misenumerations in the census by multiple imputations. In Proceedings of the Fifth Annual Research Conference, U.S. Department of Commerce 109-117. Bureau of the Census, Washington, D.C.
42. Schafer, J. L. (1991a). A comparison of the missing-data treatments in the Post-Enumeration Program. Journal of Official Statistics 7 475-498.
43. Schafer, J. L. (1991b). Algorithms for multiple imputation and posterior simulation from incomplete multivariate data with ignorable nonresponse. Ph.D. disseration, Dept. Statistics, Harvard Univ.
44. Schafer, J. L. and Schenker, N. (1991). Variance estimation with imputed means. In Proceedings of the Survey Research Methods Section 696-701. Amer. Statist. Assoc., Alexandria, VA.
45. Schenker, N. (1989). The use of imputed probabilities for missing binary data. In Proceedings of the Fifth Annual Research Conference 133-139. U.S. Bureau of the Census, Washington, D.C.
46. Schenker, N., Treiman, D. J. and Weidman, L. (1988). Evaluation of multiply-imputed public-use tapes. In Proceedings of the Survey Research Methods Section Annual Meetings 85-92. Amer. Statist. Assoc., Alexandria, VA.
47. Schenker, N., Treiman, D. J. and Weidman, L. (1993). Analyses of public use decennial census data with multiply imputed industry and occupation codes. J. Roy. Statist. Soc. Ser. C 42 545-556.
48. Schenker, N. and Welsh, A. H. (1988). Asymptotic results for multiple imputation. Ann. Statist. 16 1550-1566.
49. Stigler, S. M. (1986). The History of Statistics: The Measurement of Uncertainty before 1900. Belknap, Cambridge, MA.
50. Tanner, M. A. and Wong, W. H. (1987). The calculation of posterior distributions by data augmentation (with discussion). J. Amer. Statist. Assoc. 82 528-550.
51. Taylor, J. M. G., Muñoz, A., Bass, S. M., Chmiel, J. S., Kingsley, L. A. and Saab, A. J. (1990). Estimating the distribution of times from HIV seroconversion to AIDS using multiple imputation. Statistics in Medicine 9 505-514.
52. Treiman, D. J., Bielby, W. and Cheng, M. (1988). Evaluatinga multiple imputation method for recalibrating 1970 U.S. Census detailed industry codes to the 1980 standard. Sociological Methodology 18 309-345.
53. Tschuprow, A. A. (1923). On the mathematical expectation of the moments of frequency distributions in the case of correlated observations. Metron 2 461-493, 646-680.
54. Tu, X. M., Meng, X. L. and Pagano, M. (1993a). The AIDS epidemic: estimating survival after AIDS diagnosis from surveillance data. J. Amer. Statist. Assoc. 88 26-36.
55. Tu, X. M., Meng, X. L. and Pagano, M. (1993b). Survival differences and trends in patients with AIDS in the United States. Journal of Acquired Immune Deficiency Syndromes 6 1150-1156.
56. Wei, G. C. G. and Tanner, M. A. (1990). A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. J. Amer. Statist. Assoc. 85 699-704.
57. Weld, L. H. (1987). Significance levels from public-use data with multiply-imputed industry codes. Ph.D. dissertation, Dept. Statistics, Harvard Univ.
58. Zaslavsky, A. M. (1989). Representing census undercount: a comparison of reweighting and multiple imputation methods. Ph.D. dissertation, Dept. Mathematics, MIT.