Chapter 4 References

[1] Papageorgiou, G., Grant, S. W., Takkenberg, J. J. M., & 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

[2] Azur, M. J., Stuart, E. A., Frangakis, C., & Leaf, P. J. (2011). Multiple imputation by chained equations: what is it and how does it work?. International journal of methods in psychiatric research, 20(1), 40–49. https://doi.org/10.1002/mpr.329

[3] Badr, W. (2019). Retrieved from 6 Different Ways to Compensate for Missing Values In a Dataset (Data Imputation with examples). Towards Data Science. https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779

[4] Obadia, Y. (2017). The use of KNN for missing values. Towards Data Science. https://towardsdatascience.com/the-use-of-knn-for-missing-values-cf33d935c637

[5] Zhang, Z. (2016). Missing data imputation: Focusing on single imputation. Annals of translational medicine, 4(1). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4716933/