方法对比

	捕获-再捕获种群估计	泊松回归与负二项回归	Fay-Herriot模型（小区域估计）
领域	调查方法论	计量经济学	调查方法论
方法族	Regression model	Regression model	Regression model
起源年份	1978	1998	1979
提出者	Otis, Burnham, White & Anderson	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	Robert Fay & Roger Herriot
类型	Probabilistic population size estimator	Generalized linear model for count data	Model-based survey estimator
开创性文献	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.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.	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.
别名	Mark-Recapture, Tag-Recapture, Mark-Release-Recapture, İşaretle-Yeniden Yakala	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	SAE, Model-Based Small Area Estimation, Area-Level Model, Küçük Alan Tahmini
相关	2	4	2
摘要	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.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.	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.