Ake, C. (2005). Rounding after multiple imputationwith non-binary categorical covariates. Paper presented at SAS Users Group International, Thirty annual conference, Philadelphia. SAS Institute Inc., Cary, NC, 2005
Allison, P. (2005). Imputation of categorical variables with PROC MI. Paper presented at SAS Users Group International, Thirty annual conference, Philadelphia. SAS Institute Inc., Cary, NC, 2005
Beunckens, C., Sotto, C., Molenberghs, G. (2008).Asimulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data. Computational Statistics and Data Analysis 52:1533-1548
Birhanu, T., Molenberghs, G., Sotto, C., Kenward, M. G. (2011). Doubly robust and multipleimputation- based generalized estimating equations. Journal of Biopharmaceutical Statistics 21:202-225
Fay, R. E. (1992). When are Inferences from Multiple Imputation Valid?Washington, DC: U.S. Bureau of the Census
Goodnight, J. H. (1979). A tutorial on the SWEEP operator. The American Statistician 33:149-158
Graham, J. W., Olchowski, A. E., Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science 8:206-213
Horton, N., Lipsitz S., Parzen, M. (2003). A potential for bias when rounding in multiple imputation. The American Statistician 57:229-232
Ibrahim, N., Suliadi, S. (2011). Generating correlated discrete ordinal data using R and SAS IML. Computer Methods and Programs in Biomedicine 104:122-132
Lee, A. J. (1997). Some simplemethods for generating correlated categorical variates. Computational Statistics and Data Analysis 26:133-148
Liang, K.-Y., Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73:13-22
Lipsitz, S. R., Kim, K., Zhao, L. (1994). Analysis of repeated categorical data using generalized estimating equations. Statistics in Medicine 13:1149-1163
Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 88:125-134
Little, R. J. A. (1995). Modelling the drop-out mechanism in repeated measures studies. Journal of the American Statistical Association 90:1112-1121
Little, R. J. A., Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley
McCullagh, P. (1980). Regression models for ordinal data (with discussion). Journal of the Royal Statistical Society, Series B 42:109-142
Meng, X. L. (1994). Multiple-imputation inferences with uncongenial sources of input (with discussion). Statistical Science 9:538-573
Robins, J. M., Rotnitzky, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association 90:122-129
Robins, J. M., Rotnitzky, A., Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90:106-121
Rubin, D. B. (1976). Inference and missing data. Biometrika 63:581-592
Rubin, D. B. (1978).Multiple imputation in sample surveys - a phenomenological Bayesian approach to nonresponse. In: Proceedings of the Survey Research Methods Section, American Statistical Association, pp. 20-34. Washington, DC: American Statistical Association
Rubin, D. B. (1987). Multiple imputation for Nonresponse in Survey. New York: Wiley
Rubin, D. B. (1996). Multiple imputation after 18+years. Journal of the American Statistical Association 91:473-489
Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall
Tanner, M. A., Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of American Statistical Association 82:528-550
Williamson, J., Lipsitz, S., Kim, K. (1999).GEECAT andGEEGOR: Computer programs for the analysis of correlated categorical response data. Computer Methods and Programs in Biomedicine 58:25-34