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

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  • Urko Aguirre-Larracoechea

    (Research Unit, Osakidetza Basque Health Service, Barrualde-Galdakao Integrated Health Organisation, Galdakao-Usansolo Hospital, 48960 Galdakao, Spain
    Kronikgune Institute for Health Services Research, 48902 Barakaldo, Spain
    Research Network in Health Services in Chronic Diseases (Red de Investigación en Servicios de Salud en Enfermedades Crónicas, REDISSEC), 48960 Galdakao, Spain)

  • Cruz E. Borges

    (Deusto Institute of Technology, Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain)

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.

Suggested Citation

  • Urko Aguirre-Larracoechea & Cruz E. Borges, 2021. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches," Mathematics, MDPI, vol. 9(17), pages 1-27, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2081-:d:623959
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    References listed on IDEAS

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