Comparer des méthodes

	Age-Crime Curve Modeling	Régression binomiale négative
Domaine	Criminology	Économétrie
Famille	Regression model	Regression model
Année d'origine	1983	2011
Auteur d'origine	Travis Hirschi & Michael Gottfredson; David Farrington	Hilbe (textbook treatment); generalized linear model framework
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.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Alias	Age-Crime Relationship Modeling, Age-Offending Curve, Aggregate Age-Crime Distribution, Crime-Age Profile Modeling	NB regression, NB2 regression, negatif binom regresyonu
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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.