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| Multiple Imputation× | Small Area Estimation (Fay-Herriot Model)× | |
|---|---|---|
| Fagområde≠ | Statistik | Surveymetodologi |
| Familie≠ | Process / pipeline | Regression model |
| Oprindelsesår≠ | 1987 | 1979 |
| Ophavsperson≠ | Donald B. Rubin | Robert Fay & Roger Herriot |
| Type≠ | Missing-data handling procedure | Model-based survey estimator |
| Oprindelig kilde≠ | Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley. DOI ↗ | Fay, R. E., & Herriot, R. A. (1979). Estimates of income for small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association, 74(366), 269–277. DOI ↗ |
| Aliasser≠ | MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE) | SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini |
| Relaterede≠ | 1 | 2 |
| Resumé≠ | 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. | Small Area Estimation (SAE) refers to statistical techniques that produce reliable estimates for subpopulations — geographical regions, demographic groups, or administrative units — where direct survey samples are too sparse to yield acceptable precision. The Fay-Herriot model, introduced by Robert Fay and Roger Herriot in 1979, is the canonical area-level SAE model. It supplements weak direct survey estimates with auxiliary covariate information through an empirical Bayes or BLUP framework, substantially reducing mean squared error for small domains. |
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