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Springer International Publishing
Reference13 articles.
1. Han, J., Kamber, M., Pei, J.: 3 - Data preprocessing. In: Han, J., Kamber, M., Pei, J. (eds.) Data Mining, 3rd edn., pp. 83–124. Morgan Kaufmann (2012). https://doi.org/10.1016/B978-0-12-381479-1.00003-4
2. Schmidt, D., Niemann, M., Lindemann-Von Trzebiatowski, G.: The handling of missing values in medical domains with respect to pattern mining algorithms. In: CEUR Workshop Proceedings, vol. 1492 (2015)
3. Enders, C.K., Craig, K.: Applied Missing Data Analysis. The Guilford Press. New York, London (2010)
4. Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976). https://doi.org/10.1093/biomet/63.3.581
5. Bartlett, J.W., Harel, O., Carpenter, J.R.: Asymptotically unbiased estimation of exposure odds ratios in complete records logistic regression. Am. J. Epidemiol. 182(8), 730–736 (2014). https://doi.org/10.1093/aje/kwv114
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