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| 표본 가중치 부여 및 보정× | Fay-Herriot 모형 (Small Area Estimation, SAE)× | |
|---|---|---|
| 분야 | 조사방법론 | 조사방법론 |
| 계열≠ | Process / pipeline | Regression model |
| 기원 연도≠ | 2010 | 1979 |
| 창시자≠ | Sharon Lohr | Robert Fay & Roger Herriot |
| 유형≠ | Estimation adjustment procedure | Model-based survey estimator |
| 원전≠ | Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5 | 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 ↗ |
| 별칭 | Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket) | SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini |
| 관련≠ | 3 | 2 |
| 요약≠ | Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be. | 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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