Evaluation of imputation techniques with varying percentage of missing data

Autor:	Sangari, Seema, Ray, Herman E.
Rok vydání:	2021
Předmět:	Statistics - Methodology Statistics - Other Statistics
Zdroj:	CSP2022 Section, JSM Proceedings, 2022, pp. 640-654
Druh dokumentu:	Working Paper
Popis:	Missing data is a common problem which has consistently plagued statisticians and applied analytical researchers. While replacement methods like mean-based or hot deck imputation have been well researched, emerging imputation techniques enabled through improved computational resources have had limited formal assessment. This study formally considers five more recently developed imputation methods: Amelia, Mice, mi, Hmisc and missForest - compares their performances using RMSE against actual values and against the well-established mean-based replacement approach. The RMSE measure was consolidated by method using a ranking approach. Our results indicate that the missForest algorithm performed best and the mi algorithm performed worst. Comment: 16 pages, 21 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.04227 Zobrazit plný text záznamu View this record from Arxiv