Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| MICE× | Multiple Imputation× | |
|---|---|---|
| Vakgebied | Statistiek | Statistiek |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2011 | 1987 |
| Grondlegger≠ | Stef van Buuren & Karin Groothuis-Oudshoorn | Donald B. Rubin |
| Type≠ | Iterative multiple imputation algorithm | Missing-data handling procedure |
| Oorspronkelijke bron≠ | van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. DOI ↗ | Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley. DOI ↗ |
| Aliassen≠ | Fully Conditional Specification, Sequential Regression Multivariate Imputation, Chained Equations Imputation, Zincirleme Denklemlerle Çoklu Atama | MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE) |
| Verwant≠ | 3 | 1 |
| Samenvatting≠ | Multivariate Imputation by Chained Equations (MICE) is an iterative procedure for handling missing data in multivariate datasets. Introduced by Stef van Buuren and Karin Groothuis-Oudshoorn through the R package mice (2011), the algorithm fills each missing variable using a separate regression model conditioned on all other variables, cycling through variables repeatedly until the imputed values converge. The result is m completed datasets that are analysed separately and combined using Rubin's rules. | Multiple Imputation (MI), formally introduced by Donald B. Rubin in 1987, is a principled statistical procedure for handling missing data. Rather than replacing each missing value once, MI fills the gaps m times — each time drawing plausible values from the posterior predictive distribution of the missing data — producing m complete datasets. Each dataset is analysed independently, and the results are combined into a single set of estimates using Rubin's pooling rules. The MICE variant (Multivariate Imputation by Chained Equations), popularised by van Buuren and Groothuis-Oudshoorn (2011), extends the approach to mixed variable types by imputing each variable in turn through a sequence of conditional regression models. |
| ScholarGateGegevensset ↗ |
|
|