Imputing Missing Data in One-Shot Devices Using Unsupervised Learning Approach

Hon Yiu So (), Man Ho Ling and Narayanaswamy Balakrishnan
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Hon Yiu So: Department of Mathematics and Statistics, Oakland University, Rochester, MI 48309, USA
Man Ho Ling: Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China
Narayanaswamy Balakrishnan: Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4K1, Canada

Mathematics, 2024, vol. 12, issue 18, 1-33

Abstract: One-shot devices are products that can only be used once. Typical one-shot devices include airbags, fire extinguishers, inflatable life vests, ammo, and handheld flares. Most of them are life-saving products and should be highly reliable in an emergency. Quality control of those productions and predicting their reliabilities over time is critically important. To assess the reliability of the products, manufacturers usually test them in controlled conditions rather than user conditions. We may rely on public datasets that reflect their reliability in actual use, but the datasets often come with missing observations. The experimenter may lose information on covariate readings due to human errors. Traditional missing-data-handling methods may not work well in handling one-shot device data as they only contain their survival statuses. In this research, we propose Multiple Imputation with Unsupervised Learning (MIUL) to impute the missing data using Hierarchical Clustering, k-prototype, and density-based spatial clustering of applications with noise (DBSCAN). Our simulation study shows that MIUL algorithms have superior performance. We also illustrate the method using datasets from the Crash Report Sampling System (CRSS) of the National Highway Traffic Safety Administration (NHTSA).

Keywords: one-shot devices; missing data; clustering; imputation; inverse probability weighting; unsupervised learning; clustering; k-prototype; DBSCAN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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