השוואת שיטות

	אומדני M (רגרסיה רובסטית)	רגרסיית ריבועים זעירים חתוכים (Least Trimmed Squares - LTS)	רגרסיית קוונטילים
תחום	סטטיסטיקה	סטטיסטיקה	אקונומטריקה
משפחה	Regression model	Regression model	Regression model
שנת המקור	2009	1984	1978
הוגה השיטה	Peter J. Huber	Peter J. Rousseeuw	Koenker & Bassett
סוג	Robust linear regression	Robust linear regression	Conditional quantile regression
מקור מכונן	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.	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.
כינויים	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	LTS, least trimmed squares regression, trimmed least squares, robust regression	conditional quantile regression, regression quantiles, Kantil Regresyon
קשורות	5	5	5
תקציר	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.	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.