Generalizing Univariate Predictive Mean Matching to Impute Multiple Variables Simultaneously
| Autor: | Cai, Mingyang, van Buuren, Stef, Vink, Gerko, Arai, Kohei, Leerstoel van Buuren, Methodology and statistics for the behavioural and social sciences |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Intelligent Computing-Proceedings of the 2022 Computing Conference, 506 LNNS, 75. Springer Science and Business Media Deutschland GmbH Lecture Notes in Networks and Systems ISBN: 9783031104602 |
| ISSN: | 2367-3370 |
| Popis: | Predictive mean matching (PMM) is an easy-to-use and versatile univariate imputation approach. It is robust against transformations of the incomplete variable and violation of the normal model. However, univariate imputation methods cannot directly preserve multivariate relations in the imputed data. We wish to extend PMM to a multivariate method to produce imputations that are consistent with the knowledge of derived data (e.g., data transformations, interactions, sum restrictions, range restrictions, and polynomials). This paper proposes multivariate predictive mean matching (MPMM), which can impute incomplete variables simultaneously. Instead of the normal linear model, we apply canonical regression analysis to calculate the predicted value used for donor selection. To evaluate the performance of MPMM, we compared it with other imputation approaches under four scenarios: 1) multivariate normal distributed data, 2) linear regression with quadratic terms; 3) linear regression with interaction terms; 4) incomplete data with inequality restrictions. The simulation study shows that with moderate missingness patterns, MPMM provides plausible imputations at the univariate level and preserves relations in the data. |
| Databáze: | OpenAIRE |
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