Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Оценка численности популяции методом повторных отловов× | Оценка для малых территорий (модель Фэя-Херриота)× | |
|---|---|---|
| Область | Методология опросов | Методология опросов |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1978 | 1979 |
| Автор метода≠ | Otis, Burnham, White & Anderson | Robert Fay & Roger Herriot |
| Тип≠ | Probabilistic population size estimator | Model-based survey estimator |
| Основополагающий источник≠ | Otis, D. L., Burnham, K. P., White, G. C., & Anderson, D. R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs, 62, 3–135. link ↗ | 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 ↗ |
| Другие названия | Mark-Recapture, Tag-Recapture, Mark-Release-Recapture, İşaretle-Yeniden Yakala | SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini |
| Связанные | 2 | 2 |
| Сводка≠ | Capture-recapture (also known as mark-recapture) is a statistical method for estimating the size of an unknown population by sampling it twice and tracking which individuals appear in both samples. Formally systematized for closed animal populations by Otis, Burnham, White, and Anderson in their landmark 1978 Wildlife Monographs paper, the method extends naturally to human populations, epidemiology, and incomplete administrative records. | 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. |
| ScholarGateНабор данных ↗ |
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