References
- Andridge, R.R. 2011. “Quantifying the Impact of Fixed Effects Modeling of Clusters in Multiple Imputation for Cluster Randomized Trials.” Biometrical Journal 53: 57-74. Doi: http://dx.doi.org/10.1002/bimj.201000140.
- Barnard, J. and D.B. Rubin. 1999. “Small-Sample Degrees of Freedom With Multiple Imputation.” Biometrika 86: 949-955.
- Box, G.E.P. and G.C. Tiao. 1973. Bayesian Inference in Statistical Analysis. NewYork: Wiley.
- Cochran, W.G. 1977. Sampling Techniques. New York: Wiley.
- Graham, J., A. Olchowsi, and T. Gilreath. 2007. “How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory.” Prevention Science 8: 206-213. Doi: http://dx.doi.org/10.1007/s11121-007-0070-9.
- Harel, O. 2007. “Inferences on Missing Information Under Multiple Imputation and Two- Stage Multiple Imputation.” Statistical Methodology 4: 75-89. Doi: http://dx.doi.org/10.1016/j.stamet.2006.03.002.
- Harel, O. and X.H. Zhou. 2007. “Multiple Imputation: Review of Theory, Implementation, and Software.” Statistics in Medicine 26: 3057-3077. Doi: http://dx.doi.org/10.1002/sim.2787.
- Gelman, A., J.B. Carlin, H.S. Stern, and D.B. Rubin. 2004. Bayesian Data Analysis, 2nd ed. New York: CRC Press.
- Kim, J.K. and J. Shao. 2014. Statistical Methods for Handling Incomplete Data. Boca Raton: CRC Press.
- Kish, L. 1965. Survey Sampling. New York: Wiley.
- Lewis, T., E. Goldberg, N. Schenker, V. Beresovsky, S. Schappert, S. Decker, N. Sonnenfeld, and I. Shimizu. 2014. “The Relative Impacts of Design Effects and Multiple 21 Imputation on Variance Estimates: A Case Study With the 2008 National Ambulatory Medical Care Survey.” Journal of Official Statistics 30: 147-161. Doi: http://dx.doi.org/10.2478/jos-2014-0008.
- Little, R.J.A. and D.B. Rubin. 2002. Statistical Analysis of Missing Data. New York: Wiley.
- Lohr, S.L. 2014. “Design Effects for a Regression Slope in a Cluster Sample.” Journal of Survey Statistics and Methodology 2: 97-125. Doi: http://dx.doi.org/10.1093/jssam/smu003.
- Meng, X.L. 1995. “Multiple Imputation With Uncongenial Sources of Input (with discussion).” Statistical Science 10: 538-573.
- Reiter, J.P., T.E. Raghunathan, and S.K. Kinney. 2006. “The Importance of Modeling the Sampling Design in Multiple Imputation for Missing Data.” Survey Methodology 32: 143-149.
- Rubin, D.B. 1978. “Multiple Imputations in Sample Surveys - a Phenomenological Bayesian Approach to Nonresponse.” In Proceedings of the Survey Research Methods Section of the American Statistical Association, date and place, 20-34.
- Rubin, D.B. 1987. Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
- Rubin, D.B. and N. Schenker. 1986. “Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse.” Journal of the American Statistical Association 81: 366-374. Doi: http://dx.doi.org/10.1080/01621459.1986.10478280.
- Schafer, J.L. and R.M. Yucel. 2002. “Computational Strategies for Multivariate Linear Mixed-Effects Models With Missing Values.” Journal of Computational and Graphical Statistics 11: 421-442. Doi: http://dx.doi.org/10.1198/106186002760180608.
- Schenker, N., T.E. Raghunathan, P.L. Chiu, D.M. Makuc, G. Zhang, and A.J. Cohen. 2006. “Multiple Imputation of Missing Income Data in the National Health Interview Survey.” Journal of the American Statistical Association 101: 924-933. Doi: http://dx.doi.org/10.1198/016214505000001375.
- Skinner, C.J., D. Hold, and T.F.M. Smith. 1989. Analysis of Complex Surveys. West Sussex: Wiley.
- Valliant, R., J.A. Dever, and F. Kreuter. 2013. Practical Tools for Designing and Weighting Survey Samples. New York: Springer.
- Valliant, R., A.H. Dorfman, and R.M. Royall. 2000. Finite Population Sampling and Inference: A Prediction Approach. New York: Wiley.
- Van Buuren, S. 2012. Flexible Imputation for Missing Data. Boca Raton: CRC Press.
- Wagner, J. 2010. “The Fraction of Missing Information as a Tool for Monitoring the Quality of Survey Data”, Public Opinion Quarterly 74, 233-243. DOI: http://www.dx.doi.org/10.1093/poq/nfq007.