Article
Handling missing data for unsupervised learning with an application on a FITBIR Traumatic Brain Injury (TBI) Dataset
Mathematics and Statistics Faculty Research & Creative Works
Abstract
"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.
Meeting Name
Military Health System Research Symposium, MHSRS 2020
Department(s)
Mathematics and Statistics
Second Department
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Document Type
Poster
Document Version
Citation
File Type
text
Language(s)
English
Publication Date
6-15-2020
Disciplines
Citation Information
Louis Steinmeister, Dacosta Yeboah, Gayla Olbricht, Tayo Obafemi-Ajayi, et al.. "Handling missing data for unsupervised learning with an application on a FITBIR Traumatic Brain Injury (TBI) Dataset" (2020) Available at: http://works.bepress.com/gayla-olbricht/56/
This research was sponsored by the Missouri University of Science and Technology Mary K. Finley Endowment and Intelligent Systems Center; the Army Research Laboratory (ARL) and the Lifelong Learning Machines program from DARPA/MTO, and it was accomplished under Cooperative Agreement Number W911NF-18-2-0260.
This abstract was accepted for a poster presentation at the Military Health System Research Symposium August 2020.