विधियों की तुलना करें

	Group-Based Trajectory Model	ग्रोथ मिक्सचर मॉडल (GMM)
क्षेत्र	Criminology	सांख्यिकी
परिवार	Regression model	Latent structure
उद्भव वर्ष	1993	1999
प्रवर्तक	Daniel S. Nagin & Kenneth C. Land	Bengt O. Muthén & Kerby Shedden
प्रकार	Finite-mixture model of longitudinal developmental trajectories	Latent class / longitudinal growth model
मौलिक स्रोत	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.	Muthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469.
उपनाम	GBTM, Group-Based Modeling of Development, Nagin Trajectory Model, Semiparametric Group-Based Modeling	Büyüme Karışım Modeli (Growth Mixture Model — GMM), GMM, latent class growth analysis extension, mixture latent growth curve model
संबंधित	4	5
सारांश	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.	The Growth Mixture Model, introduced by Muthén and Shedden in 1999, is a longitudinal latent variable method that identifies distinct subpopulations — latent trajectory classes — each following its own growth curve over time. It extends the standard Latent Growth Curve (LGC) model by allowing the sample to be composed of an unknown mixture of classes with different intercepts, slopes, and variance structures.