Linganisha mbinu

	Age-Crime Curve Modeling	Group-Based Trajectory Model
Nyanja	Criminology	Criminology
Familia	Regression model	Regression model
Mwaka wa asili	1983	1993
Mwanzilishi	Travis Hirschi & Michael Gottfredson; David Farrington	Daniel S. Nagin & Kenneth C. Land
Aina	Nonlinear regression modeling of the age distribution of offending	Finite-mixture model of longitudinal developmental trajectories
Chanzo asilia	Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89(3), 552–584.	Nagin, D. S., & Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model. Criminology, 31(3), 327–362.
Majina mbadala	Age-Crime Relationship Modeling, Age-Offending Curve, Aggregate Age-Crime Distribution, Crime-Age Profile Modeling	GBTM, Group-Based Modeling of Development, Nagin Trajectory Model, Semiparametric Group-Based Modeling
Zinazohusiana	4	4
Muhtasari	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.	Group-based trajectory modeling (GBTM) is a finite-mixture method that identifies clusters of individuals who follow similar developmental paths of a behavior — most famously offending — over age or time. Introduced to criminology by Daniel Nagin and Kenneth Land in 1993, it replaces the assumption of a single average trajectory with a small number of distinct latent groups, each described by its own polynomial curve and its share of the population.