So sánh phương pháp

	Hồi quy RANSAC	Hồi quy Bình phương Nhỏ nhất Cắt tỉa (Least Trimmed Squares - LTS)	Hồi quy Quantile	Ước lượng hiệp phương sai mạnh mẽ (MCD)
Lĩnh vực	Thống kê	Thống kê	Kinh tế lượng	Thống kê
Họ	Regression model	Regression model	Regression model	Regression model
Năm ra đời	1981	1984	1978	1999
Người khởi xướng	Fischler & Bolles	Peter J. Rousseeuw	Koenker & Bassett	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Loại	Robust linear regression	Robust linear regression	Conditional quantile regression	Robust multivariate location-scatter estimator
Công trình gốc	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.	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.
Tên gọi khác	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression	conditional quantile regression, regression quantiles, Kantil Regresyon	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Liên quan	5	5	5	4
Tóm tắt	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.	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.