Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Robustne konjunktuurianalüüs× | Segmenteeriv modelleerimine× | |
|---|---|---|
| Valdkond | Statistika | Statistika |
| Perekond | Latent structure | Latent structure |
| Tekkeaasta≠ | 1990s–2000s | 1894 |
| Looja≠ | Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework | Karl Pearson |
| Tüüp≠ | Preference decomposition / stated preference | Latent variable / density estimation |
| Algallikas≠ | 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 ↗ | McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268 |
| Rööpnimetused≠ | robust CA, outlier-resistant conjoint analysis, robust stated preference analysis | finite mixture model, mixture distribution model, FMM, model-based clustering |
| Seotud≠ | 4 | 6 |
| Kokkuvõte≠ | 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. | Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components. |
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