Comparar métodos

	Análisis Conjunto Robusto	Modelado de mezclas
Campo	Estadística	Estadística
Familia	Latent structure	Latent structure
Año de origen	1990s–2000s	1894
Autor original	Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework	Karl Pearson
Tipo	Preference decomposition / stated preference	Latent variable / density estimation
Fuente seminal	Croux, C., Filzmoser, P., & Oliveira, M. R. (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems, 87(2), 218–225.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Alias	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	finite mixture model, mixture distribution model, FMM, model-based clustering
Relacionados	4	6
Resumen	Robust conjoint analysis decomposes respondent preferences for multi-attribute products or services into part-worth utilities while guarding against the distorting influence of outlying ratings or unusual respondents. It adapts classical conjoint estimation with robust regression or robust aggregation techniques so that conclusions about attribute importance remain trustworthy even when a minority of evaluations deviate markedly from the majority.	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.