1. Aerts, M., Claeskens, G., Hens, N., and Molenberghs, G. (2002). Local Multiple Imputation. Biometrika, 89(2), 375-388. [ DOI:10.1093/biomet/89.2.375] 2. Alwateer, M., Atlam, E. S., Abd El-Raouf, M. M., Ghoneim, O. A., and Gad, I. (2024). Missing Data Imputation: A Comprehensive Review. Journal of Computer and Communications, 12(11), 53-75. [ DOI:10.4236/jcc.2024.1211004] 3. Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., and Tabona, O. (2021). A Survey on Missing Data in Machine Learning. Journal of Big Data, 8(1), 1-37. [ DOI:10.1186/s40537-021-00516-9] [ PMID] [ ] 4. Graham, J. W., Olchowski, A. E., and Gilreath, T. D. (2007). How Many Imputations Are Really Needed? Some Practical Clarifications of Multiple Imputation Theory. Prevention Science, 8, 206-213. [ DOI:10.1007/s11121-007-0070-9] [ PMID] 5. Laqueur, H. S., Shev, A. B., and Kagawa, R. M. (2022). SuperMICE: An Ensemble Machine Learning Approach to Multiple Imputation by Chained Equations. American Journal of Epidemiology, 191(3), 516-525. [ DOI:10.1093/aje/kwab271] [ PMID] 6. Little, R. J. (1988). Missing-data Adjustments in Large Surveys. Journal of Business and Economic Statistics, 6(3), 287-296.
https://doi.org/10.2307/1391881 [ DOI:10.1080/07350015.1988.10509663] 7. Marshall, A., Altman, D. G., Royston, P., and Holder, R. L. (2010). Comparison of Techniques for Handling Missing Covariate Data Within Prognostic Modelling Studies: A Simulation Study. BMC Medical Research Methodology, 10(1), 1-16. [ DOI:10.1186/1471-2288-10-7] [ PMID] [ ] 8. Nadaraya, E. A. (1964). On Estimating Regression. Theory of Probability and Its Applications, 9(1), 141-142. [ DOI:10.1137/1109020] 9. Quinlan, J. R. (1987). Simplifying Decision Trees. International Journal of Man-Machine Studies, 27(3), 221-234. [ DOI:10.1016/S0020-7373(87)80053-6] 10. Raghunathan, T. E., Lepkowski, J. M., Van Hoewyk, J.,and Solenberger, P. (2001). A Multivariate Technique for Multiply Imputing Missing Values Using a Sequence of Regression Models. Survey Methodology, 27(1), 85-96. 11. Rubin, D. B. Multiple Imputation for Nonresponse in Surveys.Toronto, ON, Canada: John Wiley and Sons, Inc.; 2004. 12. Stekhoven, D. J., & Bühlmann, P. (2012). MissForest-non-parametric Missing Value Imputation for Mixed-type Data. Bioinformatics, 28(1), 112-118. [ DOI:10.1093/bioinformatics/btr597] [ PMID] 13. Tiwaskar, S., Rashid, M., and Gokhale, P. (2024). Impact of Machine Learning-based Imputation Techniques on Medical Datasets-a Comparative Analysis, {it Multimedia Tools and Applications}, DOI:10.1007/s11042-024-19103-0. [ DOI:10.1007/s11042-024-19103-0] 14. Van Buuren, S. (2018). Flexible Imputation of Missing Data. CRC press. [ DOI:10.1201/9780429492259] 15. Van Buuren, S., and Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45, 1-67. [ DOI:10.18637/jss.v045.i03] 16. Van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super Learner, Statistical Applications in Genetics and Molecular Biology, 6(1), DOI:10.2202/1544-6115.1309. [ DOI:10.2202/1544-6115.1309] |
