Linganisha mbinu

	Uchanganuzi Imara wa Madarasa ya Siri	Uundaji wa Mchanganyiko
Nyanja	Takwimu	Takwimu
Familia	Latent structure	Latent structure
Mwaka wa asili	2000s	1894
Mwanzilishi	Building on Hennig (2004) and Vermunt & Magidson (2004)	Karl Pearson
Aina	Robust latent variable / mixture model	Latent variable / density estimation
Chanzo asilia	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Majina mbadala	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	finite mixture model, mixture distribution model, FMM, model-based clustering
Zinazohusiana	6	6
Muhtasari	Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities.	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.