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  • Abidin, N. Z., Ismail, A. R., & Emran, N. A. (2018). Performance analysis of machine learning algorithms for missing value imputation. International Journal of Advanced Computer Science and Applications, 9(6), 442-447.

  • Aljuaid, T., & Sasi, S. (2016). Proper imputation techniques for missing values in data sets. In International Conference on Data Science and Engineering (ICDSE) (pp. 1-5). IEEE Conference Publication. https://doi.org/10.1109/ICDSE.2016.7823957

  • Ayilara, O. F., Zhang, L., Sajobi, T. T., Sawatzky, R., Bohm, E., & Lix, L. M. (2019). Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry. Health and Quality of Life Outcomes, 17(1), 106. https://doi.org/10.1186/s12955-019-1181-2

  • Barnett, A. G., McElwee, P., Nathan, A., Burton, N. W., & Turrell, G. (2017). Identifying patterns of item missing survey data using latent groups: An observational study. BMJ Open, 7(10), 1-9. https://doi.org/10.1136/bmjopen-2017-017284

  • Bhati, S., & Gupta, M. K. (2016). Missing data imputation for medical database: Review. International Journal of Advanced Research in Computer Science and Software Engineering, 6(4), 754-758.

  • Buuren, S. V., & Groothuis-Oudshoorn, K. (2010). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 1-68.

  • Chaudhry, A., Li, W., Basri, A., & Patenaude, F. (2019). A method for improving imputation and prediction accuracy of highly seasonal univariate data with large periods of missingness. Wireless Communications and Mobile Computing, 2019, 1-13. https://doi.org/10.1155/2019/4039758

  • Cheema, J. R. (2014). Some general guidelines for choosing missing data handling methods in educational research. Journal of Modern Applied Statistical Methods, 13(2), 53-75. https://doi.org/10.22237/jmasm/1414814520

  • Chhabra, G., Vashisht, V., & Ranjan, J. (2017). A comparison of multiple imputation methods for data with missing values. Indian Journal of Science and Technology, 10(19), 1-7. https://doi.org/10.17485/ijst/2017/v10i19/110646

  • Dettori, J. R., Norvell, D. C., & Chapman, J. R. (2018). The sin of missing data: Is all forgiven by way of imputation? Global Spine Journal, 8(8), 892-894. https://doi.org/10.1177/2192568218811922

  • Dong, Y., & Peng, C. Y. J. (2013). Principled missing data methods for researchers. SpringerPlus, 2(1), 1-17. https://doi.org/10.1186/2193-1801-2-222

  • Fichman, M., & Cummings, J. N. (2003). Multiple imputation for missing data: Making the most of what you know. Organizational Research Methods, 6(3), 282-308. https://doi.org/10.1177/1094428103255532

  • Gad, A. M., & Abdelkhalek, R. H. M. (2017). Imputation methods for longitudinal data: A comparative study. International Journal of Statistical Distributions and Applications, 3(4), 72. https://doi.org/10.11648/j.ijsd.20170304.13

  • Gopal, K. M., Durgaprasad, N., Deepa, K. S., Sravan, R. G., & Revanth, R. D. (2019). Comparative analysis of different imputation techniques for handling missing dataset. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(7), 347-351.

  • Goretzko, D., Heumann, C., & Bühner, M. (2019). Investigating parallel analysis in the context of missing data: A simulation study comparing six missing data methods. Educational and Psychological Measurement, 80(4), 756-774. https://doi.org/10.1177/0013164419893413

  • Grund, S., Lüdtke, O., & Robitzsch, A. (2018). Multiple imputation of missing data at level 2: A comparison of fully conditional and joint modeling in multilevel designs. Journal of Educational and Behavioral Statistics, 43(3), 316-353. https://doi.org/10.3102/1076998617738087

  • Hughes, R. A., Heron, J., Sterne, J. A., & Tilling, K. (2019). Accounting for missing data in statistical analyses: Multiple imputation is not always the answer. International Journal of Epidemiology, 48(4), 1294-1304. https://doi.org/10.1093/ije/dyz032

  • Jadhav, A., Pramod, D., & Ramanathan, K. (2019). Comparison of performance of data imputation methods for numeric dataset. Applied Artificial Intelligence, 33(10), 913-933. https://doi.org/10.1080/08839514.2019.1637138

  • Kaiser, J. (2014). Dealing with missing values in data. Journal of Systems Integration, 5(1), 42- 51. http://dx.doi.org/10.20470/jsi.v5i1.178

  • Kamatchi P, L., & Baranidharan, C. (2019). Missing data imputation methods for autism prediction. International Journal of Recent Technology and Engineering, 8(5), 940-944.

  • Le, T. D., Beuran, R., & Tan, Y. (2018). Comparison of the most influential missing data imputation algorithms for healthcare. In 2018 10th International Conference on Knowledge and Systems Engineering (KSE) (pp. 247-251). IEEE Conference Publication. http://dx.doi.org/10.1109/KSE.2018.8573344

  • Li, Y., Ji, L., Oravecz, Z., Brick, T. R., Hunter, M. D., & Chow, S. M. (2019). dynr. mi: An R program for multiple imputation in dynamic modeling. World Academy of Science, Engineering and Technology, 13(5), 302-311. https://doi.org/10.5281/zenodo.3298841

  • Little, R. J. (1988). A test of missing completely at random for multivariate data with missing values. Journal of The American Statistical Association, 83(404), 1198-1202.

  • Little, R. J., & Rubin, D. B. (1987). Statistical analysis with missing data. John Wiley & Sons.

  • Lo, A. W., Siah, K. W., & Wong, C. H. (2019). Machine learning with statistical imputation for predicting drug approvals. Harvard Data Science Review, 1(1), 1-25. https://doi.org/10.1162/99608f92.5c5f0525

  • Ma, Z., & Chen, G. (2018). Bayesian methods for dealing with missing data problems. Journal of The Korean Statistical Society, 47(3), 297-313. https://doi.org/10.1016/j.jkss.2018.03.002

  • Madley-Dowd, P., Hughes, R., Tilling, K., & Heron, J. (2019). The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of Clinical Epidemiology, 110, 63-73. https://doi.org/10.1016/j.jclinepi.2019.02.016

  • Malarvizhi, M. R., & Thanamani, A. S. (2012). K-Nearest Neighbor in missing data imputation. International Journal of Engineering Research and Development, 5(1), 5-7.

  • Masconi, K. L., Matsha, T. E., Echouffo-Tcheugui, J. B., Erasmus, R. T., & Kengne, A. P. (2015). Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: A systematic review. The EPMA Journal, 6(1), 1-11. https://doi.org/10.1186/s13167-015-0028-0

  • Newman, D. A. (2003). Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organizational Research Methods, 6(3), 328-362. https://doi.org/10.1177/1094428103254673

  • Newman, D. A. (2014). Missing data: Five practical guidelines. Organizational Research Methods, 17(4), 372-411. https://doi.org/10.1177/1094428114548590

  • Nwakuya, M. T., & Nwabueze, J. C. (2018). Comparison of shrinkage–based estimators in the presence of missing data: A multiple imputation analysis. International Journal of Statistics and Applications, 8(6), 305-308. https://doi.org/10.5923/j.statistics.20180806.03

  • Ochieng’Odhiambo, F. (2020). Comparative study of various methods of handling missing data. Mathematical Modelling and Applications, 5(2), 87.

  • Pampaka, M., Hutcheson, G., & Williams, J. (2016). Handling missing data: Analysis of a challenging data set using multiple imputation. International Journal of Research & Method in Education, 39(1), 19-37. https://doi.org/10.1080/1743727X.2014.979146

  • Papageorgiou, G., Grant, S. W., Takkenberg, J. J., & Mokhles, M. M. (2018). Statistical primer: How to deal with missing data in scientific research? Interactive Cardiovascular and Thoracic Surgery, 27(2), 153-158. https://doi.org/10.1093/icvts/ivy102

  • Pedersen, A. B., Mikkelsen, E. M., Cronin-Fenton, D., Kristensen, N. R., Pham, T. M., Pedersen, L., & Petersen, I. (2017). Missing data and multiple imputation in clinical epidemiological research. Clinical Epidemiology, 9, 157-166. https://doi.org/10.2147/CLEP.S129785

  • Ratolojanahary, R., Ngouna, R. H., Medjaher, K., Junca-Bourié, J., Dauriac, F., & Sebilo, M. (2019). Model selection to improve multiple imputation for handling high rate missingness in a water quality dataset. Expert Systems with Applications, 131, 299-307. https://doi.org/10.1016/j.eswa.2019.04.049

  • Salgado C. M., Azevedo C., Proença H., & Vieira S. M. (2016) Missing data. In Secondary analysis of electronic health records (pp. 143-162). Springer.

  • Scheffer, J. (2002). Dealing with missing data. Research Letters in the Information and Mathematical Sciences, 3, 153-160.

  • Schmitt, P., Mandel, J., & Guedj, M. (2015). A comparison of six methods for missing data imputation. Journal of Biometrics & Biostatistics, 6(1), 1-6. https://doi.org/10.472/2155-6180.1000224

  • Shi, D., Lee, T., Fairchild, A. J., &Maydeu-Olivares, A. (2019). Fitting ordinal factor analysis models with missing data: A comparison between pairwise deletion and multiple imputation. Educational and Psychological Measurement, 80(1), 41-66. https://doi.org/10.1177/0013164419845039

  • Sim, J., Lee, J. S., & Kwon, O. (2015). Missing values and optimal selection of an imputation method and classification algorithm to improve the accuracy of ubiquitous computing applications. Mathematical Problems in Engineering, 2015, 1-14. https://doi.org/10.1155/2015/538613

  • Song, Q., & Shepperd, M. (2007). Missing data imputation techniques. International Journal of Business Intelligence and Data Mining, 2(3), 261-291. https://doi.org/10.1504/IJBIDM.2007.015485

  • Stavseth, M. R., Clausen, T., &Røislien, J. (2019). How handling missing data may impact conclusions: A comparison of six different imputation methods for categorical questionnaire data. SAGE Open Medicine, 7, 1-12. https://doi.org/10.1177/2050312118822912

  • Stekhoven, D. J., & Bühlmann, P. (2012). MissForest - Non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112-118. https://doi.org/10.1093/bioinformatics/btr597

  • Sullivan, T. R., White, I. R., Salter, A. B., Ryan, P., & Lee, K. J. (2018). Should multiple imputation be the method of choice for handling missing data in randomized trials? Statistical Methods in Medical Research, 27(9), 2610-2626. https://doi.org/10.1177/0962280216683570

  • Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Pearson.

  • Turner, E. L., Yao, L., Li, F., & Prague, M. (2019). Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness. Statistical Methods in Medical Research, 29(5), 1338-1353. https://doi.org/10.1177/0962280219859915

  • Van Buuren, S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research, 16(3), 219-242. https://doi.org/10.1177/0962280206074463

  • Van Buuren, S., Boshuizen, H. C., & Knook, D. L. (1999). Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in medicine, 18(6), 681-694. https://doi.org/10.1002/(SICI)1097-0258(19990330)18:6<681::AID-SIM71>3.0.CO;2-R

  • van Ginkel, J. R., Linting, M., Rippe, R. C., & van der Voort, A. (2019). Rebutting existing misconceptions about multiple imputation as a method for handling missing data. Journal of Personality Assessment, 102(3), 297-308. https://doi.org/10.1080/00223891.2018.1530680

  • Wah, Y. B., Ibrahim, N., Hamid, H. A., Abdul-Rahman, S., & Fong, S. (2018). Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy. Pertanika Journal of Science & Technology, 26, 329-340.

  • Wilks, S. S. (1932). Certain generalizations in the analysis of variance. Biometrika, 24(3/4), 471-494. https://doi.org/10.2307/2331979

  • Yadav, M. L., & Roychoudhury, B. (2018). Handling missing values: A study of popular imputation packages in R. Knowledge-Based Systems, 160, 104-118. https://doi.org/10.1016/j.knosys.2018.06.012

  • Zhang, Z. (2016). Missing data imputation: focusing on single imputation. Annals of Translational Medicine, 4(1), 1-9. https://doi.org/10.3978/j.issn.2305-5839.2015.12.38

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