Comparer des méthodes

	Estimation de la taille de population par capture-recapture	Régression de Poisson et binomiale négative
Domaine	Méthodologie d'enquête	Économétrie
Famille	Regression model	Regression model
Année d'origine	1978	1998
Auteur d'origine	Otis, Burnham, White & Anderson	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Probabilistic population size estimator	Generalized linear model for count data
Source fondatrice	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.
Alias	Mark-Recapture, Tag-Recapture, Mark-Release-Recapture, İşaretle-Yeniden Yakala	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	2	4
Résumé	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.