Salīdzināt metodes

	Robust Conjoint Analysis	Robustā diskriminējošā analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Regression model
Izcelsmes gads	1990s–2000s	1997
Autors	Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework	Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)
Tips	Preference decomposition / stated preference	Robust classification / discriminant analysis
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.	Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143.
Citi nosaukumi	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi
Saistītās	4	5
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.	Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001).