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Robust klyngeanalyse (TCLUST)×W-Estimator Robust Regression (Welsch / Tukey Bisquare)×
FagområdeStatistikStatistik
FamilieRegression modelRegression model
Oprindelsesår20081974
OphavspersonGarcía-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST)Beaton & Tukey (bisquare weight); Welsch (Welsch weight)
TypeRobust model-based clusteringRobust regression (redescending M-estimator)
Oprindelig kildeGarcía-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 ↗
AliasserTCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST)Tukey bisquare M-estimator, Welsch M-estimator, redescending M-estimator, W-Tahmin Edici (Welsch / Tukey Bisquare)
Relaterede54
ResuméRobust 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.
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ScholarGateSammenlign metoder: Robust Cluster Analysis · W-Estimator. Hentet 2026-06-17 fra https://scholargate.app/da/compare