Confronta i metodi

	Regressione RANSAC	Regression con Minimi Quadrati Trimmatizzati (Least Trimmed Squares, LTS)
Campo	Statistica	Statistica
Famiglia	Regression model	Regression model
Anno di origine	1981	1984
Ideatore	Fischler & Bolles	Peter J. Rousseeuw
Tipo	Robust linear regression	Robust linear regression
Fonte seminale	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.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.
Alias	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression
Correlati	5	5
Sintesi	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.	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.