方法对比

	回归的Tau (τ)估计量	最小裁剪平方和（LTS）回归
领域	统计学	统计学
方法族	Regression model	Regression model
起源年份	1988	1984
提出者	Yohai & Zamar	Peter J. Rousseeuw
类型	Robust linear regression	Robust linear regression
开创性文献	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.
别名	tau regression estimator, robust tau regression, Tau-Tahmin Edici	LTS, least trimmed squares regression, trimmed least squares, robust regression
相关	4	5
摘要	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.