IVEware User Guide Bibliography

  1. Atkinson, A. C. (1985). Plots, transformations and regression: An introduction to graphical methods of diagnostic regression analysis. Oxford: Clarendon Press.
  2. Bondarenko, I. & Raghunathan, T. E. (2010). Multiple imputation for causal inference. Department of Biostatistics, University of Michigan,Ann Arbor, 1001(48109)
  3. 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.
  4. Dong, Q., Elliott, M. R. & Raghunathan, T. E. (2014a). A nonparametric method to generate synthetic populations to adjust for complex sampling design features. Survey Methodology, 40(1), 29-46.
  5. Dong, Q., Elliott, M. R. & Raghunathan, T. E. (2014b). Combining information from multiple complex surveys. Survey Methodology, 40, 347-354.
  6. Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (1995). Bayesian data analysis. London: Chapman and Hall.
  7. Gelman, A. & Hill, J. (2006). Data analysis using regression and Multilevel/Hierarchical models. New York: Cambridge University Press.
  8. He, Y. & Raghunathan, T. E. (2006). Tukey's gh distribution for multiple imputation. The American Statistician, 60, 251-256: Response.
  9. 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,
  10. Kish, L. & Frankel, M. (1974). Inference from complex systems. Journal of the Royal Statistical Society.Series B (Methodological), 36(1), 1-37.
  11. 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.
  12. Little, R. J. A., Liu, F. & Raghunathan, T. E. (2004). Statistical Disclosure Techniques Based on Multiple Imputation. P.p. 141-152 in Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin’s Statistical Family (A. Gelman & X.-L. Meng, eds.).
  13. 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.
  14. Raghunathan, T. E., Lepkowski, J. M., Hoewyk, J. V. & Solenberger, P. (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27(1), 85-95.
  15. Raghunathan, T. E., Reiter, J. P. & Rubin, D. B. (2003). Multiple imputation for statistical disclosure limitation. Journal of Official Statistics-Stockholm-, 19(1), 1-16.
  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.
  17. Raghunathan, T. E. (2015). Missing data analysis in practice. Boca Raton: CRC Press.
  18. Raghunathan, T. E, Berglund, P., and Solenberger, P. W. (2018). Multiple Imputation in Practice: With Examples Using IVEware. Boca Raton: CRC Press
  19. Reiter, J. (2002). Satisfying disclosure restrictions with synthetic data sets. Journal of Official Statistics, 18(4), 531-543.
  20. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592.
  21. Rubin, D. B. (1987b). Multiple imputation for nonresponse in surveys. New York: Wiley.
  22. 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.
  23. van Buuren, S. (2012). Flexible imputation of missing data. Boca Raton: Chapman and Hall/CRC.
  24. van Buuren, S. & Oudshoorn, K. (1999). Flexible multivariate imputation by MICE. Technical Report, Leiden: TNO Preventie En Gezondheid, TNO/VGZ/PG 99.054.
  25. Vittinghoff, E., Glidden, D. V., Shiboski, S. C. & McCulloch, C. E. (2005). Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models. New York: Springer Science & Business Media
  26. Weisberg, S. (2013). Applied linear regression (4th ed.) Hoboken, NJ: Wiley.
  27. Zhou, H., Elliott, M. R. & Raghunathan, T. E. (2016a). Synthetic Multiple-Imputation Procedure for Multistage Complex Samples. Journal of Official Statistics, 32, 231-256.
  28. Zhou, H., Elliott, M. R.& Raghunathan, T. E. (2016b). Multiple imputation in two-stage cluster samples using the weighted finite population bayesian bootstrap. Journal of Survey Statistics and Methodology, 4, 139-170.
  29. Zhou, H., Elliott, M. R. & Raghunathan, T. E. (2015). A two-step semiparametric method to accommodate sampling weights in multiple imputation. Biometrics, 72, 242-252.

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