Σύγκριση μεθόδων

	Παλινδρόμηση RANSAC	Εκτίμηση Εύρωστων Συνδιακυμάνσεων (MCD)
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1981	1999
Δημιουργός	Fischler & Bolles	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Τύπος	Robust linear regression	Robust multivariate location-scatter estimator
Θεμελιώδης πηγή	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. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Εναλλακτικές ονομασίες	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Συναφείς	5	4
Σύνοψη	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.	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.