مقایسهٔ روش‌ها

	رگرسیون حداقل مربعات هرس‌شده (LTS)	رگرسیون حداقل مربعات معمولی (OLS)	تخمین کوواریانس مقاوم (MCD)
حوزه	آمار	اقتصادسنجی	آمار
خانواده	Regression model	Regression model	Regression model
سال پیدایش	1984	2019	1999
پدیدآور	Peter J. Rousseeuw	Wooldridge (textbook treatment); classical least squares	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
نوع	Robust linear regression	Linear regression	Robust multivariate location-scatter estimator
منبع بنیادین	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
نام‌های دیگر	LTS, least trimmed squares regression, trimmed least squares, robust regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
مرتبط	5	5	4
خلاصه	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation.