השוואת שיטות

	רגרסיית ריבועים זעירים חתוכים (Least Trimmed Squares - LTS)	רגרסיית קוונטילים	אמידת שונוּת-משותפת חסונה (MCD)
תחום	סטטיסטיקה	אקונומטריקה	סטטיסטיקה
משפחה	Regression model	Regression model	Regression model
שנת המקור	1984	1978	1999
הוגה השיטה	Peter J. Rousseeuw	Koenker & Bassett	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
סוג	Robust linear regression	Conditional quantile regression	Robust multivariate location-scatter estimator
מקור מכונן	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
כינויים	LTS, least trimmed squares regression, trimmed least squares, robust regression	conditional quantile regression, regression quantiles, Kantil Regresyon	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
קשורות	5	5	4
תקציר	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.