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| Robust conjoint analysis× | Robust kanonisk korrelationsanalys (Robust CCA)× | |
|---|---|---|
| Ämnesområde | Statistik | Statistik |
| Familj | Latent structure | Latent structure |
| Ursprungsår≠ | 1990s–2000s | 2003 |
| Upphovsperson≠ | Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework | Croux & Dehon (building on Hotelling's CCA framework) |
| Typ≠ | Preference decomposition / stated preference | Robust multivariate association |
| Ursprungskälla≠ | 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. DOI ↗ | Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569. link ↗ |
| Alias≠ | robust CA, outlier-resistant conjoint analysis, robust stated preference analysis | Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation |
| Närliggande | 4 | 4 |
| Sammanfattning≠ | 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. |
| ScholarGateDatamängd ↗ |
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