A review of hot deck imputation for survey non-response

International Statistical Review

Volumn 78, Issue 1, 2010, Pages 40-64

  Andridge, Rebecca R ^a   Little, Roderick J A ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Item non response; Missing data; Multiple imputation; Variance estimation

Indexed keywords

EID: 77955807436     PISSN: 03067734     EISSN: 17515823     Source Type: Journal    
DOI: 10.1111/j.1751-5823.2010.00103.x     Document Type: Review

References (95)

1. Andridge RR, Little RJA. The use of sample weights in hot deck imputation. J. Official Stat. 2009, 25:21-36.
2. Bailar JC, Bailar BA. Comparison of two procedures for imputing missing survey values. ASA Proc. Section on Survey Res. Methods 1978, 462-467. pp
3. Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics 2005, 61:962-972.
4. Bankier M, Fillion JM, Luc M, Nadeau C. Imputing numeric and qualitative variables simultaneously. ASA Proc. Section on Survey Res. Methods 1994, 242-247. pp
5. Bankier M, Luc M, Nadeau C, Newcombe P. Additional details on imputing numeric and qualitative variables simultaneously. ASA Proc. Section on Survey Res. Methods 1995, 287-292. pp
6. Bankier M, Poirier P, Lachance M, Mason P. A generic implementation of the nearest-neighbour imputation methodology (nim). Proceedings of the Second International Conference on Establishment Surveys 2000, 571-578. pp
7. Barzi F, Woodward M. Imputations of missing values in practice: Results from imputations of serum cholesterol in 28 cohort studies. Amer. J. Epidemiol. 2004, 160:34-45.
8. Berger YG, Rao JNK. Adjusted jackknife for imputation under unequal probability sampling without replacement. J. Roy. Statist. Soc. Ser. B 2006, 68:531-547.
9. Bollinger CR, Hirsch BT. Match bias from earnings imputation in the current population survey: The case of imperfect matching. J. Labor Econ. 2006, 24:483-519.
10. 2003 NSDUH Methodological Resource Book 2005, Bowman KChromy JHunter SMartin POdom D. Eds., Rockville, MD, Substance Abuse and Mental Health Services Administration, Office of Applied Studies
11. Breiman L, Friedman JH. Classification and Regression Trees 1993, New York, Chapman & Hall
12. Brick JM, Kalton G. Handling missing data in survey research. Stat. Meth. Med. Res. 1996, 5:215-238.
13. Brick JM, Kalton G, Kim JK. Variance estimation with hot deck imputation using a model. Surv. Methodol. 2004, 30:57-66.
14. Burns RM. Multiple and replicate item imputation in a complex sample survey. U.S. Bureau of the Census Proceedings of the Sixth Annual Research Conference 1990, 655-665. pp
15. Chen J, Shao J. Inference with survey data imputed by hot deck when imputed values are nonidentifiable. Statist. Sinica. 1999, 9:361-384.
16. Chen J, Shao J. Nearest neighbor imputation for survey data. J. Official. Stat. 2000, 16:113-141.
17. Chen J, Shao J. Jackknife variance estimation for nearest-neighbor imputation. J. Amer. Statist. Assoc. 2001, 96:260-269.
18. Cochran WG. Sampling Techniques 1977, 3rd ed., New York, Wiley
19. Cotton C. Functional description of the generalized edit and imputation system. 1991, Tech. rep., Statistics Canada
20. Cox BG. The weighted sequential hot deck imputation procedure. ASA Proc. Section on Survey Res. Methods 1980, 721-726. pp
21. Cox BG, Folsom RE. An evaluation of weighted hot deck imputation for unreported health care visits. ASA Proc. Section on Survey Res. Methods 1981, 412-417. pp
22. David M, Little RJA, Samuhel ME, Triest RK. Alternative methods for CPS income imputation. J. Amer. Statist. Assoc. 1986, 81:29-41.
23. Efron B. Missing data, imputation, and the bootstrap. J. Amer. Statist. Assoc. 1994, 89:463-475.
24. England AM, Hubbell KA, Judkins DR, Ryaboy S. Imputation of medical cost and payment data. ASA Proc. Section on Survey Res. Methods 1994, 406-411. pp
25. Ezzati-Rice TM, Fahimi M, Judkins D, Khare M. Serial imputation of nhanes III with mixed regression and hot-deck imputation. ASA Proc. Section on Survey Res. Methods 1993a, 292-296. pp
26. Ezzati-Rice TM, Khare M, Rubin DB, Little RJA, Schafer JL. A comparison of imputation techniques in the third national health and nutrition examination survey. ASA Proc. Section on Survey Res. Methods 1993b, 303-308. pp
27. Fay RE. Valid inferences from imputed survey data. ASA Proc. Section on Survey Res. Methods 1993, 41-48. pp
28. Fay RE. Alternative paradigms for the analysis of imputed survey data. J. Amer. Statist. Assoc. 1996, 91:490-498.
29. Fay RE. Theory and application of nearest neighbor imputation in Census 2000. ASA Proc. Section on Survey Res. Methods 1999, 112-121 pp
30. Fellegi IP, Holt D. A systematic approach to automatic edit and imputation. J. Amer. Statist. Assoc. 1976, 71:17-35.
31. Ford BL. An overview of hot-deck procedures. Incomplete Data in Sample Surveys 1983, Vol. 2:185-207. Madow WGOlkin IRubin DB. Eds. New York, Academic Press
32. Grau EA, Frechtel PA, Odom DM. A simple evaluation of the imputation procedures used in HSDUH. ASA Proc. Section on Survey Res. Methods 2004, 3588-3595. pp
33. Haziza D, Beaumont JF. On the construction of imputation classes in surveys. Int. Statist. Rev. 2007, 75:25-43.
34. Haziza D, Rao JNK. A nonresponse model approach to inference under imputation for missing survey data. Surv. Method. 2006, 32:53-64.
35. Heitjan DF, Little RJA. Multiple imputation for the fatal accident reporting system. Appl. Stat. 1991, 40:13-29.
36. Herzog TN, Scheuren FJ, Winkler WE. Data Quality and Record Linkage Techniques 2009, New York, Springer
37. Judkins DR. Imputing for swiss cheese patterns of missing data. Proceedings of Statistics Canada Symposium 97 1997, In
38. Judkins DR, Hubbell KA, England AM. The imputation of compositional data. ASA Proc. Section on Survey Res. Methods 1993, 458-462. pp
39. Kalton G, Kasprzyk D. The treatment of missing survey data. Surv. Method. 1986, 12:1-16.
40. Kass GV. An exploratory technique for investigating large quantities of categorical data. Appl. Stat. 1980, 29:119-127.
41. Khare M, Little RJA, Rubin DB, Schafer JL. Multiple imputation of nhanes III. ASA Proc. Section on Survey Res. Methods 1993, 297-302. pp
42. Kim JK. A note on approximate bayesian bootstrap. Biometrika 2002, 89:470-477.
43. Kim JK, Brick JM, Fuller WA, Kalton G. On the bias of the multiple-imputation variance estimator in survey sampling. J. Roy. Statist. Soc. Ser. B 2006, 68:509-521.
44. Kim JK, Fuller W. Fractional hot deck imputation. Biometrika 2004, 91:559-578.
45. Lazzeroni LG, Schenker N, Taylor JMG. Robustness of multiple-imputation techniques to model misspecification. ASA Proc. Section on Survey Res. Methods 1990, 260-265. pp
46. Lillard L, Smith JP, Welch F. What do we really know about wages: The importance of non-reporting and census imputation. 1982, Tech. rep., Rand Corporation, Santa Monica, CA
47. Little RJ, Yosef M, Cain KC, Nan B, Harlow SD. A hot-deck multiple imputation procedure for gaps in longitudindal data on recurrent events. Stat. Med. 2008, 27:103-120.
48. Little RJA. Survey nonresponse adjustments for estimates of means. Int. Statist. Rev. 1986, 54:139-157.
49. Little RJA. Missing-data adjustments in large surveys. J. Buss. Econ. Stat. 1988, 6:287-296.
50. Little RJA, An H. Robust likelihood-based analysis of multivariate data with missing values. Statist. Sinica. 2004, 14:949-968.
51. Little RJA, Rubin DB. Statistical Analysis with Missing Data 2002, 2nd ed., New York, Wiley
52. Little RJA, Vartivarian S. On weighting the rates in non-response weights. Stat. Med. 2003, 22:1589-1599.
53. Little RJA, Vartivarian S. Does weighting for nonresponse increase the variance of survey means. Surv. Method. 2005, 31:161-168.
54. Marker DA, Judkins DR, Winglee M. Large-scale imputation for complex surveys. Survey Nonresponse 2002, 329-341 New York, Wiley
55. Meng XL. Multiple imputation inferences with uncongenial sources of input (with discussion). Stat. Sci. 1994, 9:538-573.
56. NCES statistical standards. 2002, National Center for Education Statistics, Tech. rep., U.S. Department of Education
57. Oh HL, Scheuren FJ. Weighting adjustments for unit nonresponse. Incomplete Data in Sample Surveys 1983, Vol. 2:143-184. Madow WG, Olkin IRubin DB. Eds., New York, Academic Press
58. Ono M, Miller HP. Income nonresponses in the current population survey. ASA Proc. Social Statistics Section 1969, 277-288. pp
59. Perez A, Dennis RJ, Gil JFA, Rondon MA. Use of the mean, hot deck and multiple imputation techniques to predict outcome in intensive care unit patients in colombia. Stat. Med. 2002, 21:3885-3896.
60. Platek R, Gray GB. Imputation methodology: Total survey error. Incomplete Data in Sample Surveys 1983, Vol. 2:249-333. Madow WG, Olkin IRubin DB. Eds., New York, Academic Press
61. R: A Language and Environment for Statistical Computing 2007, http://www.R-project.org, R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0
62. Raghunathan TE, Lepkowski JM, Van Hoewyk J, Solenberger P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv. Method. 2001, 21:85-95.
63. Rancourt E. Estimation with nearest neighbor imputation at Statistics Canada. ASA Proc. Section on Survey Res. Methods 1999, 131-138. pp
64. Rancourt E, Särndal CE, Lee H. Estimation of the variance in the presence of nearest neighbor imputation. ASA Proc. Section on Survey Res. Methods 1994, 888-893. pp
65. Rao JNK. On variance estimation with imputed survey data. J. Amer. Stat. Assoc. 1996, 91:499-506.
66. Rao JNK, Shao J. Jackknife variance estimation with survey data under hot deck imputation. Biometrika 1992, 79:811-822.
67. Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors and not always observed. J. Amer. Statist. Assoc. 1994, 89:846-866.
68. Robins JM, Rotnitzky A, Zhao LP. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J. Amer. Statist. Assoc. 1995, 90:106-121.
69. Robins JM, Wang N. Inference for imputation estimators. Biometrika 2000, 87:113-124.
70. Rubin DB. Inference and missing data (with discussion). Biometrika 1976, 63:581-592.
71. Rubin DB. Multiple imputation in sample surveys - a phenomenological Bayesian approach to nonresponse. ASA Proc. Section on Survey Res. Methods 1978, 20-34. pp
72. Rubin DB. The bayesian bootstrap. Ann. Stat. 1981, 9:130-134.
73. Rubin DB. Statistical matching using file concatenation with adjusted weights and multiple imputations. J. Bus. Econ. Stat. 1986, 4:87-94.
74. Rubin DB. Multiple Imputation for Nonresponse in Surveys 1987, New York, Wiley
75. Rubin DB. Multiple imputation after 18+ years. J. Amer. Stat. Assoc. 1996, 91:473-489.
76. Rubin DB, Schenker N. Multiple imputation for interval estimation from simple random samples with ignorable non-response. J. Amer. Stat. Assoc. 1986, 81:366-374.
77. Saigo H, Shao J, Sitter RR. A repeated half-sample bootstrap and balanced repeated replications for randomly imputed data. Surv. Method. 2001, 27:189-196.
78. Särndal CE. Methods for estimating the precision of survey estimates when imputation has been used. Surv. Method. 1992, 18:241-252.
79. Schenker N, Taylor JMG. Partially parametric techniques for multiple imputation. Comput. Statist. Data Anal. 1996, 22:425-446.
80. Shao J, Chen J. Approximate balanced half sample and repeated replication methods for imputed survey data. Sankhya Ser. B 1999, 61:187-201.
81. Shao J, Chen Y, Chen Y. Balanced repeated replication for stratified multistage survey data under imputation. J. Amer. Stat. Assoc. 1998, 93:819-831.
82. Shao J, Sitter RR. Bootstrap for imputed survey data. J. Amer. Stat. Assoc. 1996, 91:1278-1288.
83. Shao J, Steel P. Variance estimation for survey data with composite imputation and nonnegligible sampling fractions. J. Amer. Stat. Assoc. 1999, 94:254-265.
84. Shao J, Wang H. Sample correlation coefficients based on survey data under regression imputation. J. Amer. Stat. Assoc. 2002, 97:544-552.
85. Siddique J, Belin TR. Multiple imputation using an iterative hot-deck with distance-based donor selection. Stat. Med. 2008, 27:83-102.
86. Srivastava MS, Carter EM. The maximum likelihood method for non-response in sample surveys. Surv. Method. 1986, 12:61-72.
87. Tang L, Song J, Belin TR, Unutzer J. A comparison of imputation methods in a longitudinal randomized clinical trial. Stat. Med. 2005, 24:2111-2128.
88. Twisk J, de Vente W. Attrition in longitudinal studies: How to deal with missing data. J. Clin. Epidemiol. 2002, 55:329-337.
89. Technical paper 63. 2002, U.S. Bureau of the Census, Tech. rep., U.S. Government Printing Office
90. A comparison study of acs if-then-else, nim, discrete edit and imputation systems using acs data. UN/ECE Work Session of Statistical Data Editing 2003, U.S. Bureau of the Census, Madrid, Spain
91. Plan and operation of the third national health and nutrition examination survey, 1988-94. 1994, U.S. Department of Health and Human Services, Tech. rep., National Center for Health Statistics, Centers for Disease Control and Prevention
92. Third national health and nutrition examination survey (nhanes iii, 1988-1994): Multiply imputed data set. 2001, U.S. Department of Health and Human Services, cd-rom, series 11, no. 7a. Tech. rep., National Center for Health Statistics, Centers for Disease Control and Prevention
93. Van Buuren S, Oudshoorn CGM. Flexible multivariate imputation by MICE. 1999, Tech. rep., TNO Prevention and Health, Leiden
94. Williams RL, Folsom RE. Weighted hot-deck imputation of medical expenditures based on a record check subsample. ASA Proc. Section on Survey Res. Methods 1981, 406-411. pp
95. Zhang G, Little RJA. Extensions of the penalized spline of propensity prediction method of imputation. Biometrics 2009, 65:911-918.