Salīdzināt metodes

	Robust Conjoint Analysis	Robustā kanoniskās korelacijas analīze (Robust CCA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1990s–2000s	2003
Autors	Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework	Croux & Dehon (building on Hotelling's CCA framework)
Tips	Preference decomposition / stated preference	Robust multivariate association
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.	Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569.
Citi nosaukumi	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation
Saistītās	4	4
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 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.