"The problem of missing data and imputation have been widely discussed amongst specialists. However, many data scientists and applied statisticians fail to appropriately consider this issue. Often, it seems intuitive to discard observations containing missing data or simply to substitute means. This can lead to disastrous consequences, particularly in an era of exponentially increasing data volumes. In the following, we show how inappropriate handling of missing data and an insufficient analysis of the censoring mechanism can lead to a bias, overconfidence in the estimation of parameters, could challenge the reproducibility of obtained results, and may distort the structure of the dataset."--Background.
L. Steinmeister et al., "Handling missing data for unsupervised learning with an application on a FITBIR Traumatic Brain Injury (TBI) Dataset,", Jun 2020.
Military Health System Research Symposium, MHSRS 2020
Mathematics and Statistics
Electrical and Computer Engineering
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research