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W-оценка (Welsch / Tukey Bisquare) в робастной регрессии×S-оценка для робастной регрессии×
ОбластьСтатистикаСтатистика
СемействоRegression modelRegression model
Год появления19741984
Автор методаBeaton & Tukey (bisquare weight); Welsch (Welsch weight)Rousseeuw & Yohai (1984)
ТипRobust regression (redescending M-estimator)Robust linear regression
Основополагающий источникBeaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16(2), 147-185. DOI ↗Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer. DOI ↗
Другие названияTukey bisquare M-estimator, Welsch M-estimator, redescending M-estimator, W-Tahmin Edici (Welsch / Tukey Bisquare)S-estimation, robust S-regression, S-Tahmin Edici
Связанные45
СводкаThe 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.The S-estimator is a robust linear-regression method, introduced by Rousseeuw and Yohai in 1984, that estimates the coefficients by minimising a robust M-estimate of the residual scale rather than the variance of the residuals. By driving down a bounded measure of residual spread it can attain a breakdown point of up to 50%, so it stays reliable even when a large share of the data are outliers, and it provides the first stage of the well-known MM-estimator.
ScholarGateНабор данных
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  2. 2 Источники
  3. PUBLISHED
  1. v1
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ScholarGateСравнение методов: W-Estimator · S-Estimator. Получено 2026-06-18 из https://scholargate.app/ru/compare