Little, RJA, Rubin, DB. Statistical Analysis with Missing Data. New York: Wiley; 1987.
Little, RJA, Rubin, DB. Statistical Analysis with Missing Data. 2nd ed. Hoboken: Wiley; 2002.
Zhang, JL, Rubin, DB. Estimation of causal e?ects via principal stratification when some outcomes are truncated by “death”. J Educ Behav Stat 2003, 28:353–368.
Frumento, P, Mealli, F, Pacini, B, Rubin, DB. Evaluating the effect of training on wages in the presence of noncompliance, nonemployment, and missing outcome data. J Am Stat Assoc 2012, 498:450–466.
Madow, WG, Nisselson, H, Olkin, I. Incomplete Data in Sample Surveys, Volume 1: Report and Case Studies. New York: Academic Press; 1983.
Madow, WG, Olkin, I, Rubin, DB. Incomplete Data in Sample Surveys, Volume 2: Theory and Bibliographies. New York: Academic Press; 1983.
Madow, WG, Olkin, I. Incomplete Data in Sample Surveys, Volume 3: Proceedings of the Symposium. New York: Academic Press; 1983.
Groves, RM, Dillman, DA, Eltinge, JL, Little, RJA. Survey Nonresponse. New York: Wiley; 2002.
Efron, B. Missing data, imputation, and the bootstrap. J Am Stat Assoc 1994, 89:463–479.
Rubin, DB. Discussion: missing data, imputation, and the bootstrap. J Am Stat Assoc 1994, 89:457–478.
Rubin, DB. Inference and missing data. Biometrika 1976, 63:581–590.
Shafer, JL, Graham, JW. Missing data: our view of the state of the art. Psychol Methods 2002, 7:147–177.
Shih, WJ. On informative and random dropouts in longitudinal studies. Biometrics 1992, 48:970–972.
Rubin, DB. A note on Bayesian, likelihood, and sampling distribution inferences. J Educ Stat 1978, 3:189–201.
Heitjan, DF. Annotation: what can be done about missing data? Approaches to imputation. Am J Public Health 1997, 87:548–550.
Rubin, DB, Stern, HS, Vehovar, V. Handling “Don`t Know” survey responses: the case of the Slovenian Plebiscite. J Am Stat Assoc 1995, 90:822–828.
Baker, SG, Fitzmaurice, GM, Freedman, LS, Kramer, BS. Simple adjustments for randomized trials with nonrandomly missing or censored outcomes arising from informative covariates. Biostatistics 2006, 7:29–40.
Rubin, DB. Statistical matching using file concatenation with adjusted weights and multiple imputations. J Bus Econ Stat 1986, 4:87–95.
Little, RJA. Adjustments in large surveys. J Bus Econ Stat 1988, 6:287–296.
Schafer, JL, Schenker, N. Inference with imputed conditional means. J Am Stat Assoc 2000, 95:144–154.
Lee, H, Rancourt, E, Särndal, CE. Variance estimation for survey data under single imputation. In: Groves, RM, Dillman, DA, Eltinge, JL, Little, RJA, eds. Survey Nonresponse. New York: Wiley; 2002, 315–328.
Shao, J. Replication methods for variance estimation in complex sample surveys with imputed data. In: Groves, RM, Dillman, DA, Eltinge, JL, Little, RJA, eds. Survey Nonresponse. New York: Wiley; 2002, 303–314.
Rubin, DB. Multiple Imputation for Nonresponse in Surveys. New York: Wiley; 1987.
Rubin, DB. Multiple imputation after 18+ years. J Am Stat Assoc 1996, 91:473–489.
Rubin, DB, Schenker, N. Interval estimation from multiply imputed data: a case study using census agriculture industry codes. J Off Stat 1987, 3:375–387.
Rubin, DB, Schenker, N. Multiple imputation for interval estimation from simple random samples with ignorable nonresponse. J Am Stat Assoc 1986, 81:366–374.
Rubin, DB, Schenker, N. Multiple imputation in health‐care databases: an overview and some applications. Stat Med 1991, 10:585–598.
Dorey, FJ, Little, RJA, Schenker, N. Multiple imputation for threshold‐crossing data with interval censoring. Stat Med 1993, 12:1589–1603.
Schenker, N, Taylor, JMG. Partially parametric techniques for multiple imputation. Comput Stat Data Anal 1996, 22:425–446.
Heitjan, DF, Little, RJA. Multiple imputation for the fatal accident reporting system. J R Stat Soc C 1991, 40:13–29.
Marini, MM, Olsen, AR, Rubin, DB. Maximum‐likelihood estimation in panel studies with missing data. Sociol Methodol 1980, 11:314–357.
Tanner, MA, Wong, WH. The calculation of posterior distributions by data augmentation. J Am Stat Assoc 1987, 82:528–540.
Dempster, AP, Laird, NM, Rubin, DB. Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). J R Stat Soc B 1977, 39:1–38.
Geman, S, Geman, D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 1984, 6:721–741.
Metropolis, N, Ulam, S. The Monte Carlo method. J Am Stat Assoc 1949, 49:335–341.
Hastings, WK. Monte Carlo sampling methods using Markov Chains and their applications. Biometrika 1970, 57:97–109.
Gelman, A, Carlin, JB, Stern, HS, Rubin, DB. Bayesian Data Analysis. 2nd ed. London: Chapman %26 Hall; 2003.
Schafer, JL. Analysis of Incomplete Multivariate Data. New York: Chapman and Hall; 1997.
Kennickell, AB. Imputation of the 1989 survey of consumer finances: stochastic relaxation and multiple imputation (with discussion). Am Stat Assoc Proc Sect Surv Res Methods 1991, 1–10 (Discussion: 21–23).
Oudshoorn, CGM, Van Buuren, S. 2000. Multivariate Imputation by Chained Equations: MICE V1.0 User`s Manual. TNO Report PG/VGZ/00.038. Leiden: TNO Preventie en Gezond‐heid. http://www.stefvanbuuren.nl/publications/MICE%20V1.0%20Manual%20TNO00038%202000.pdf.
Raghunathan, TE, Lepkowski, JM, Van Hoewyk, J, Solenberger, P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol 2001, 27:85–95.
Münnich, R, Rässler, S. PRIMA: a new multiple imputation procedure for binary variables. J Off Stat 2005, 21:325–341.
Brand, JPL, Groothuis‐Oudshoorn, CGM, Rubin, DB, Van Buuren, S. Fully conditional specifications in multivariate imputation. J Stat Comput Simul 2006, 76:1049–1064.
Rubin, DB. Multiple imputation in sample surveys—a phenomenological Bayesian approach to nonresponse. Proceedings of the American Statistical Association, Section on Survey Research Methods. Alexandria, VA: American Statistical Association; 1978, 20–40.
Rubin, DB. The design of a general and flexible system for handling nonresponse in sample surveys. Am Stat 2004, 58:298–302. (First appeared in unpublished form in 1997, prepared under contract for the U.S. Social Security Administration.)
Rubin, DB. Multiple Imputation for Nonresponse in Surveys. 2nd ed. New York: Wiley; 2004.
Pramanik, S, Scheuren, F. 2012. Book Review: Drechsler, J. (2011). Generating Multiply Imputed Synthetic Datasets: Theory and Implementation, Lecture Notes in Statistics, New York: Springer. J Am Stat Assoc, 107:436–437.
Drechsler, J. Generating Multiply Imputed Synthetic Datasets: Theory and Implementation. Lecture Notes in Statistics. New York: Springer; 2011.
Drechsler, J. New data dissemination approaches in old Europe * synthetic datasets for a German establishment survey. J Appl Stat 2012, 39:243–265.
Van Buuren, S. Flexible Imputation of Missing Data. New York: Chapman and Hall; 2012.
Barnard, J, Rubin, DB. Small‐sample degrees of freedom with multiple imputation. Biometrika 1999, 86:948–955.
Meng, XL, Rubin, DB. Performing likelihood ratio tests with multiply‐imputed data sets. Biometrika 1992, 79:103–111.
Li, KH, Meng, XL, Raghunathan, TE, Rubin, DB. Significance levels from repeated p‐values with multiply‐imputed data. Stat Sin 1991, 1:65–92.
Meng, X‐L. Multiple‐imputation inferences with uncongenial sources of input (with discussion). Stat Sci 1994, 9:538–573.
Ezzati‐Rice, TM, Fahimi, M, Judkins, D, Khare, M. Serial imputation of NHANES III with mixed regression and hot‐deck techniques. Am Stat Assoc Proc Sect Surv Res Methods 1993, 1:292–296.
Rubin, DB. Nested multiple imputation of NMES via partially incompatible MCMC. Stat Neerl 2003, 57:3–18.
Kim, JK, Michael Brick, J, Fuller, WA, Kalton, G. On the bias of the multiple‐imputation variance estimator in survey sampling. J R Stat Soc B 2006, 68:509–521.
Raghunathan, TE, Solenberger, P, Van Hoewyk, J. IVEware: Imputation and Variance Estimation Software—User Guide. Michigan: Survey Methodology Program, Survey Research Center, Institute for Social Research, University of Michigan; 2002.
Horton, NJ, Kleinman, KP. Much ado about nothing: a comparison of missing data methods and software to fit incomplete data regression models. Am Stat 2007, 61:79–90.
Raghunathan, TE, Grizzle, JE. A split questionnaire survey design. J Am Stat Assoc 1995, 90:54–63.
Rässler, S. Statistical Matching: A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches. Lecture Notes in Statistics, 168, New York: Springer; 2002.
Gartner, H, Jensen, U, Rässler, S. Estimating German overqualification with stochastic earnings frontiers. AStA Adv Stat Anal 2010, 94:33–51.
Little, RJA. Statistical analysis of masked data. J Off Stat 1993, 9:407–426.
Rubin, DB. Discussion: statistical disclosure limitation. J Off Stat 1993, 9:462–468.
Raghunathan, TE, Reiter, JP, Rubin, DB. Multiple imputation for statistical disclosure limitation. J Off Stat 2003, 19:1–16.
Reiter, JP. Simultaneous use of multiple imputation for missing data and disclosure limitation. Surv Methodol 2004, 30:235–242.
Drechsler, J, Bender, S, Rässler, S. Comparing fully and partially synthetic data sets for statistical disclosure control in the German IAB Establishment Panel. Trans Data Priv 2008, 1:105–130.
Drechsler, J, Reiter, J. Disclosure risk and data utility for partially synthetic data: an empirical study using the German IAB Establishment Survey. J Off Stat 2009, 25:589–603.
Reiter, JP, Drechsler, J. Releasing multiply‐imputed, synthetic data generated in two stages to protect confidentiality. Stat Sin 2010, 20:405–421.
Rubin, DB. Formalizing subjective notions about the effect of nonrespondents in sample surveys. J Am Stat Assoc 1977, 72:538–543.
Robert, CP, Casella, G. Monte Carlo Statistical Methods. New York: Springer; 1999.
Gelman, A, Meng, XL. Applied Bayesian Modeling and Causal Inference from Incomplete‐Data Perspectives. New York: Wiley; 2004.
Cook, SR, Rubin, DB. Multiple imputation in designing medical device trials. In: Becker, KM, Whyte, JJ, eds. Clinical Evaluation of Medical Devices. Washington, DC: Humana Press; 2005.
Rässler, S, Rubin, DB, Schenker, N. Incomplete data: diagnosis, imputation, and estimation. In: de Leeuw, E, Hox, J, Dillman, D, eds. The International Handbook of Survey Research Methodology. Thousand Oaks/London/New Delhi: Sage; 2008.
Rässler, S, Rubin, DB, Zell, ER. Incomplete data in epidemiology and medical statistics. In: Rao, CR, Miller, JP, Rao, DC, eds. Handbook of Statistics 27: Epidemiology and Medical Statistics. Amsterdam: Elseiver; 2008, 569–601.
Rässler, S, Rubin, DB, Zell, ER. Imputation. In: Lavrakas, PJ, ed. Encyclopedia of Survey Research Methods. Thousand Oaks/London/New Delhi: Sage; 2008, 322–327.