Сравнение на методи

	Оценъчен метод Тау (τ) за регресия	Регресия на най-малките отрязани квадрати (LTS)
Област	Статистика	Статистика
Семейство	Regression model	Regression model
Година на възникване	1988	1984
Създател	Yohai & Zamar	Peter J. Rousseeuw
Тип	Robust linear regression	Robust linear regression
Основополагащ източник	Yohai, V. J., & Zamar, R. H. (1988). High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale. Journal of the American Statistical Association, 83(402), 406-413.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.
Други названия	tau regression estimator, robust tau regression, Tau-Tahmin Edici	LTS, least trimmed squares regression, trimmed least squares, robust regression
Свързани	4	5
Резюме	The Tau estimator is a robust linear regression method introduced by Yohai and Zamar in 1988 that fits the model by minimising an efficient τ-scale of the residuals. It builds on the scale estimate of the S-estimator to combine a high breakdown point with high statistical efficiency, and is often used as an alternative to the MM-estimator in small samples.	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.