Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Makadirio ya Ukubwa wa Idadi ya Watu kwa Njia ya Kukamata-Kukamatwa tena× | Makadirio ya Eneo dogo (Mfumo wa Fay-Herriot)× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1978 | 1979 |
| Mwanzilishi≠ | Otis, Burnham, White & Anderson | Robert Fay & Roger Herriot |
| Aina≠ | Probabilistic population size estimator | Model-based survey estimator |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | Mark-Recapture, Tag-Recapture, Mark-Release-Recapture, İşaretle-Yeniden Yakala | SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini |
| Zinazohusiana | 2 | 2 |
| Muhtasari≠ | 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. |
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