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Original Articles

Multiple Imputation of Predictor Variables Using Generalized Additive Models

, &
Pages 968-985 | Received 13 Mar 2013, Accepted 31 Mar 2014, Published online: 05 Nov 2015

References

  • Andridge, R.R., Little, R. J.A. (2010). A review of hot deck imputation for survey non-response. International Statistical Review 78:40–64.
  • de Jong, R. (2012). Robust Multiple Imputation. Ph.D. dissertation, University of Hamburg. Hamburg: Staats-und Universitaetsbibliothek. Available at: http://ediss.sub.uni-hamburg.de/volltexte/2012/5971/
  • Efron, B. (1975). The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Association 70(352):892–898.
  • Eilers, P. H.C., Marx, B.D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science 11(2):89–121.
  • Gelman, A., Hill, J., Su, Y.-S., Yajima, M., Pittau, M.G. (2010). mi: Missing Data Imputation and Model Checking. (R Package Version 0.09-11.) (Software). Retrieved from http://CRAN.R-project.org
  • Harrell, F.E. (2010). Hmisc: Harrell Miscellaneous. (R Package Version 3.8-3). (Software). Retrieved from http://CRAN.R-project.org
  • Harris, I. (1989). Predictive fit for natural exponential families. Biometrika 76(4):675–684.
  • He, Y., Raghunathan, T. (2009). On the performance of sequential regression multiple imputation methods with non normal error distributions. Communications in Statistics – Simulation and Computation 38(4):856–883.
  • Little, R. (1988). Missing-data adjustments in large surveys. Journal of Business & Economic Statistics 6(3):287–296.
  • Little, R. J.A., Rubin, D.B. (2002). Statistical Analysis with Missing Data.2nd ed. New York: John Wiley & Sons.
  • McCullagh, P., Nelder, J. (1989). Generalized Linear Models. 2nd ed., London: Chapman & Hall.
  • R Development Core Team. (2011). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Rigby, R.A., Stasinopoulos, D.M. (1996). A semi-parametric additive model for variance heterogeneity. Statistics and Computing 6(1):57–65.
  • Rigby, R.A., Stasinopoulos, D.M. (2005). Generalized additive models for location, scale and shape (with discussion). Journal of the Royal Statistical Society: Series C 54(3):507–554.
  • Rubin, D.B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business and Economic Statistics 4:87–94.
  • Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
  • Rubin, D.B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association 91(434):473–489.
  • Rubin, D.B. (2003). Discussion on multiple imputation. International Statistical Review 71(3):619–625.
  • Schafer, J.L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall.
  • Schafer, J.L., Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods 7(2):147–177.
  • Schenker, N., Taylor, J. M.G. (1996). Partially parametric techniques for multiple imputation. Computational Statistics & Data Analysis 22:425–446.
  • Spanos, A. (1995). On normality and the linear regression model. Econometric Reviews 14(2):195–203.
  • Stasinopoulos, D.M., Rigby, R.A. (2007). Generalized additive models for Location Scale and Shape (GAMLSS) in R. Journal of Statistical Software 23(7):1–46.
  • van Buuren, S., Brand, J., Groothuis-Oudshoorn, C., Rubin, D.B. (2006). Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation 76(12):1049–1064.
  • van Buuren, S., Groothuis-Oudshoorn, K. (2010). MICE: Multivariate Imputation by Chained Equations in R. (R Package Version 2.10). (Software). Retrieved from http://CRAN.R-project.org
  • von Hippel, P.T. (2009). How to impute interactions, squares, and other transformed variables. Sociological Methodology 39(1):265–291.
  • Yu, K., Jones, M.C. (2004). Likelihood-based local linear estimation of the conditional variance function. Journal of the American Statistical Association 99(465):139–144.
  • Yu, L.-M., Burton, A., Rivero-Arias, O. (2007). Evaluation of software for multiple imputation of semi-continuous data. Statistical Methods in Medical Research 16:243–258.

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