Compară metode

	Analiză conjoint robustă	Analiza de Corelație Canonică Robustă (Robust CCA)
Domeniu	Statistică	Statistică
Familie	Latent structure	Latent structure
Anul apariției	1990s–2000s	2003
Autorul original	Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework	Croux & Dehon (building on Hotelling's CCA framework)
Tip	Preference decomposition / stated preference	Robust multivariate association
Sursa seminală	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.	Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569.
Denumiri alternative	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation
Înrudite	4	4
Rezumat	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 canonical correlation analysis extends classical CCA by replacing the standard sample covariance matrix with a robust estimator — such as the Minimum Covariance Determinant (MCD) or S-estimator — so that outlying observations do not distort the estimated canonical correlations and canonical variates between two sets of variables.