เปรียบเทียบวิธี

	Group-Based Trajectory Model	โมเดลการผสมการเติบโต (Growth Mixture Model: GMM)
สาขาวิชา	Criminology	สถิติศาสตร์
ตระกูล	Regression model	Latent structure
ปีกำเนิด	1993	1999
ผู้ริเริ่ม	Daniel S. Nagin & Kenneth C. Land	Bengt O. Muthén & Kerby Shedden
ประเภท	Finite-mixture model of longitudinal developmental trajectories	Latent class / longitudinal growth model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	4	5
สรุป	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.