Сравнение методов

	Робастный конджойнт-анализ	Робастный дискриминантный анализ
Область	Статистика	Статистика
Семейство	Latent structure	Regression model
Год появления	1990s–2000s	1997
Автор метода	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)
Тип	Preference decomposition / stated preference	Robust classification / discriminant analysis
Основополагающий источник	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.
Другие названия	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi
Связанные	4	5
Сводка	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).