Imputation for repeated bounded outcome data: statistical and machine-learning approaches
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2021-09-28
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MDPI
Resumen
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.
Palabras clave
Bounded outcomes
Imputation
Machine learning
Repeated measures
Zero-one-inflated beta distribution
Imputation
Machine learning
Repeated measures
Zero-one-inflated beta distribution
Descripción
Materias
Cita
Aguirre-Larracoechea, U., & Borges, C. E. (2021). Imputation for repeated bounded outcome data: statistical and machine-learning approaches. Mathematics, 9(17). https://doi.org/10.3390/MATH9172081