Comparer des méthodes

	Régression par Moindres Carrés Trimés (LTS)	RANSAC Regression
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1984	1981
Auteur d'origine	Peter J. Rousseeuw	Fischler & Bolles
Type	Robust linear regression	Robust linear regression
Source fondatrice	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Fischler, M. A. & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381-395.
Alias	LTS, least trimmed squares regression, trimmed least squares, robust regression	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu
Apparentées	5	5
Résumé	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.	RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins the largest consensus of agreeing points.