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Title: Bounded missing data imputation using statistical and machine learning approaches Authors:  Cruz Borges - University of Deusto (Spain)
Urko Aguirre - Hospital Galdakao-Usansolo, Osakidetza (Spain) [presenting]
Abstract: Real-life data are mostly bounded and non-Gaussian variables. One of the best approaches for modelling them is the Zero-one-inflated beta (ZOIB) regression. 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 nave 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. There is no consensus among the studied methods: i-ZOIB and the machine-learning ANN, SVR and RF methods showed the best performance.