विधियों की तुलना करें

	Criminal Trajectory Clustering	Group-Based Trajectory Model
क्षेत्र	Criminology	Criminology
परिवार	Regression model	Regression model
उद्भव वर्ष	2010	1993
प्रवर्तक	Daniel S. Nagin; Christophe Genolini & Bruno Falissard (KmL)	Daniel S. Nagin & Kenneth C. Land
प्रकार	Algorithmic clustering of longitudinal offending trajectories	Finite-mixture model of longitudinal developmental trajectories
मौलिक स्रोत	Nagin, D. S. (2005). Group-Based Modeling of Development. Harvard University Press. ISBN: 9780674016866	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.
उपनाम	Offending Trajectory Clustering, Longitudinal Offending Cluster Analysis, Trajectory Shape Clustering, Crime-Curve Clustering	GBTM, Group-Based Modeling of Development, Nagin Trajectory Model, Semiparametric Group-Based Modeling
संबंधित	4	4
सारांश	Criminal trajectory clustering is the broad family of methods that group individuals by the shape of their longitudinal offending curves. Rather than committing to a single statistical model, it spans algorithmic approaches — k-means for longitudinal data, distance-based clustering of trajectory shapes, and likelihood-based latent class growth — and treats the choice of clustering method itself as a modeling decision validated by fit and stability criteria.	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.