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| Robust Cluster Analysis (TCLUST)× | W-estimaattorin robusti regressio (Welsch / Tukey Bisquare)× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2008 | 1974 |
| Kehittäjä≠ | García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST) | Beaton & Tukey (bisquare weight); Welsch (Welsch weight) |
| Tyyppi≠ | Robust model-based clustering | Robust regression (redescending M-estimator) |
| Alkuperäislähde≠ | García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis. The Annals of Statistics, 36(3), 1324-1345. DOI ↗ | Beaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16(2), 147-185. DOI ↗ |
| Rinnakkaisnimet | TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST) | Tukey bisquare M-estimator, Welsch M-estimator, redescending M-estimator, W-Tahmin Edici (Welsch / Tukey Bisquare) |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | Robust Cluster Analysis is a trimmed model-based clustering method, introduced by García-Escudero and colleagues in 2008, that partitions continuous multivariate data into clusters while resisting the influence of outliers and noise. By setting aside a fraction of the most discordant observations, it keeps the recovered cluster structure from being contaminated by stray points. | The W-estimator is a family of robust M-estimator variants for linear regression that use the Tukey bisquare and Welsch weight functions, introduced in the line of work going back to Beaton and Tukey (1974). Because its weights fall rapidly toward zero as a residual grows, it resists outliers more strongly than the Huber M-estimator. |
| ScholarGateAineisto ↗ |
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