IVEware Literature Resources
Atkinson, A. C. (1987). Plots, transformations and regression: An introduction to graphical methods of diagnostic regression analysis. Clarendon Press.
Bondarenko, I. & Raghunathan, T. E. (2010). Multiple imputation for causal inference. Section on Survey Research Methods-JSM.
Bondarenko, I. & Raghunathan, T. E. (2016). Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models. Statistics in Medicine, 35, 3007-3020.
Dong, Q., Elliott, M. R. & Raghunathan, T. E. (2014). A nonparametric method to generate synthetic populations toadjust for complex sampling design features. Survey Methodology, 40(1), 29-46.
Dong, Q., Elliott, M. R. & Raghunathan, T. E. (2014). Combining information from multiple complex surveys. Survey Methodology, 40, 347-354.
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (2013). Bayesian data analysis, Third Edition. London: Chapman and Hall.
Gelman, A. & Hill, J. (2006). Data analysis using regression and Multilevel/Hierarchical models. New York: Cambridge University Press.
He, Y. & Raghunathan, T. E. (2006). Tukey’s gh distribution for multiple imputation. The American Statistician, 60, 251-256: Response.
Heeringa, S. G., Little, R. J. A., & Raghunathan, T. E. (1997). Imputation of multivariate data on household net worth. University of Michigan, Ann Arbor, Michigan,
Kish, L. & Frankel, M. (1974). Inference from complex systems. Journal of the Royal Statistical Society. Series B (Methodological), 36(1), 1-37.
Li, K. H., Raghunathan, T. E. & Rubin, D. B. (1991). Large-sample significance levels from multiply imputed data using moment-based statistics and an F-reference distribution. Journal of the American Statistical Association, 86(416), 1065-1073.
Little, Liu and Raghunathan (2004). Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives. Hoboken, NJ: John Wiley & Sons.
Raghunathan, T. E. (1994). Monte carlo methods for exploring sensitivity to distributional assumptions in a bayesian analysis of a series of 2 x 2 tables. Statistics in Medicine, 13(15), 1525-1538.
Raghunathan, Lepkowski, Van Hoewyk and Solenberger (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27(1): 85-95.
Raghunathan, Reiter and Rubin (2003). Multiple Imputation for Statistical Disclosure Limitation. Journal of Official Statisitics, 19(1): 1-16.
Raghunathan, T. E. & Rubin, D. B. (1998). Roles for Bayesian techniques in survey sampling. Proceedings of the Silver Jubilee Meeting of the Statistical Society of Canada, 51-55.
Raghunathan, T. E. (2015). Missing data analysis in practice. Boca Raton, FL: CRC Press.
Raghunathan, T. E, Berglund, P., and Solenberger, P. W. (2017). Multiple Imputation in Practice: With Examples Using IVEware. Boca Raton, FL: CRC Press (Forthcoming).
Reiter (2002). Satisfying Disclosure Restrictions With Synthetic Data Sets. Journal of Official Statistics, 18(4): 531-543.
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.
Schenker, N., Raghunathan, T. E. & Bondarenko, I. (2010). Improving on analyses of self-reported data in a large-scale health survey by using information from an examination-based survey. Statistics in Medicine, 29(5), 533-545.
van Buuren, S. (2012). Flexible imputation of missing data. Boca Raton, FL: Chapman and Hall/CRC.