方法对比

	稳健联合分析	稳健判别分析
领域	统计学	统计学
方法族	Latent structure	Regression model
起源年份	1990s–2000s	1997
提出者	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)
类型	Preference decomposition / stated preference	Robust classification / discriminant analysis
开创性文献	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.
别名	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi
相关	4	5
摘要	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).