Vertaile menetelmiä

	RANSAC-regressio	Kvanttiiliregressio	Robustti kovarianssimenetelmä (MCD)
Tieteenala	Tilastotiede	Ekonometria	Tilastotiede
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1981	1978	1999
Kehittäjä	Fischler & Bolles	Koenker & Bassett	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Tyyppi	Robust linear regression	Conditional quantile regression	Robust multivariate location-scatter estimator
Alkuperäislähde	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Rinnakkaisnimet	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Liittyvät	5	5	4
Tiivistelmä	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.