Compară metode

	Regresia prin metoda celor mai mici pătrate trunchiate (LTS)	Regresie RANSAC
Domeniu	Statistică	Statistică
Familie	Regression model	Regression model
Anul apariției	1984	1981
Autorul original	Peter J. Rousseeuw	Fischler & Bolles
Tip	Robust linear regression	Robust linear regression
Sursa seminală	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.
Denumiri alternative	LTS, least trimmed squares regression, trimmed least squares, robust regression	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu
Înrudite	5	5
Rezumat	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.