The impact of misclassifications and outliers on imputation methods
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Authors
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Publication date
2024
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Type
01A - Journal article
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Parent work
Journal of Applied Statistics
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Series
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Volume
51
Issue / Number
14
Pages / Duration
2894-2928
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Publisher / Publishing institution
Taylor & Francis
Place of publication / Event location
London
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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.
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ISBN
ISSN
1360-0532
0266-4763
0266-4763
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Hybrid
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Citation
Templ, M., & Ulmer, M. (2024). The impact of misclassifications and outliers on imputation methods. Journal of Applied Statistics, 51(14), 2894–2928. https://doi.org/10.1080/02664763.2024.2325969