Salīdzināt metodes

	Robust Conjoint Analysis	Jaukto sadalījumu modelēšana
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1990s–2000s	1894
Autors	Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework	Karl Pearson
Tips	Preference decomposition / stated preference	Latent variable / density estimation
Pirmavots	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
Citi nosaukumi	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	finite mixture model, mixture distribution model, FMM, model-based clustering
Saistītās	4	6
Kopsavilkums	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.