Comparer des méthodes

	Age-Crime Curve Modeling	Régression de Poisson et binomiale négative
Domaine	Criminology	Économétrie
Famille	Regression model	Regression model
Année d'origine	1983	1998
Auteur d'origine	Travis Hirschi & Michael Gottfredson; David Farrington	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Nonlinear regression modeling of the age distribution of offending	Generalized linear model for count data
Source fondatrice	Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89(3), 552–584.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	Age-Crime Relationship Modeling, Age-Offending Curve, Aggregate Age-Crime Distribution, Crime-Age Profile Modeling	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	4	4
Résumé	Age-crime curve modeling fits statistical functions to the well-known relationship between age and offending: crime rises sharply in adolescence, peaks in the late teens or early twenties, and declines through adulthood. Brought to prominence by Hirschi and Gottfredson's 1983 claim that this curve is invariant, and elaborated by Farrington, the modeling task is to capture its characteristic skewed, single-peaked shape and to debate what it implies about the causes of crime.	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.