The impact of misclassifications and outliers on imputation methods

M. Templ and Markus Ulmer

Journal of Applied Statistics, 2024, vol. 51, issue 14, 2894-2928

Abstract: Many imputation methods have been developed over the years and tested mostly under ideal settings. Surprisingly, there is no detailed research on how imputation methods perform when the idealized assumptions about the distribution of data and/or model assumptions are partly not fulfilled. This research looks into the susceptibility of imputation techniques, particularly in relation to outliers, misclassifications, and incorrect model specifications. This is crucial knowledge about how well the methods convince in everyday life because, in reality, conditions are usually not ideal, and model assumptions may not hold. The data may not fit the defined models well. Outliers distort the estimates, and misclassifications reduce the quality of most imputation methods. Several different evaluation measures are discussed, from comparing imputed values with true values or comparing certain statistics, from the performance of classifiers to the variance of estimated parameters. Some well-known imputation methods are compared based on real data and simulations. It turns out that robust conditional imputation methods outperform other methods for real data and simulation settings.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2024.2325969 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:51:y:2024:i:14:p:2894-2928

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2024.2325969

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().