Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Urko Aguirre-Larracoechea and Cruz E. Borges
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Urko Aguirre-Larracoechea: Research Unit, Osakidetza Basque Health Service, Barrualde-Galdakao Integrated Health Organisation, Galdakao-Usansolo Hospital, 48960 Galdakao, Spain
Cruz E. Borges: Deusto Institute of Technology, Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain

Mathematics, 2021, vol. 9, issue 17, 1-27

Abstract: Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naïve and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBRAE) methods. The results varied depending on the missingness rate and mechanism. The i-ZOIB and the machine-learning ANN, SVR and RF methods showed the best performance.

Keywords: imputation; bounded outcomes; repeated measures; zero-one-inflated beta distribution; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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