Algorithmic-Type Imputation Techniques with Different Data Structures: Alternative Approaches in Comparison

Abstract

In recent years, with the spread availability of large datasets from multiple sources, increasing attention has been devoted to the treatment of missing information. Recent approaches have paved the way to the development of new powerful algorithmic techniques, in which imputation is performed through computer-intensive procedures. Although most of these approaches are attractive for many reasons, less attention has been paid to the problem of which method should be preferred according to the data structure at hand. This work addresses the problem by comparing the two methods missForest and IPCA with a new method we developed within the forward imputation approach. We carried out comparisons by considering different data patterns with varying skewness and correlation of variables, in order to ascertain in which situations a given method produces more satisfying results.