Сравнение методов

	RANSAC-регрессия	Регрессия по методу наименьших усеченных квадратов (LTS)	Робастная оценка ковариации (MCD)
Область	Статистика	Статистика	Статистика
Семейство	Regression model	Regression model	Regression model
Год появления	1981	1984	1999
Автор метода	Fischler & Bolles	Peter J. Rousseeuw	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Тип	Robust linear regression	Robust linear regression	Robust multivariate location-scatter estimator
Основополагающий источник	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.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Другие названия	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Связанные	5	5	4
Сводка	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.	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.