পদ্ধতির তুলনা করুন

	Group-Based Trajectory Model	Age-Crime Curve Modeling
ক্ষেত্র	Criminology	Criminology
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1993	1983
প্রবর্তক	Daniel S. Nagin & Kenneth C. Land	Travis Hirschi & Michael Gottfredson; David Farrington
ধরন	Finite-mixture model of longitudinal developmental trajectories	Nonlinear regression modeling of the age distribution of offending
মৌলিক উৎস	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.	Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89(3), 552–584.
অপর নাম	GBTM, Group-Based Modeling of Development, Nagin Trajectory Model, Semiparametric Group-Based Modeling	Age-Crime Relationship Modeling, Age-Offending Curve, Aggregate Age-Crime Distribution, Crime-Age Profile Modeling
সম্পর্কিত	4	4
সারসংক্ষেপ	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.	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.