Evaluation of imputation techniques with varying percentage of missing data

TL;DR

This paper compares five recent imputation methods for missing data, finding that missForest outperforms others in accuracy, with implications for data analysis practices.

Contribution

It provides a formal comparison of five emerging imputation techniques against traditional methods using RMSE, highlighting the superior performance of missForest.

Findings

missForest achieved the lowest RMSE among methods

mi algorithm performed the worst in accuracy

performance varied with percentage of missing data

Abstract

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.