قارن الطرق

	انحدار RANSAC	تقدير التغاير المتين (MCD)
المجال	الإحصاء	الإحصاء
العائلة	Regression model	Regression model
سنة النشأة	1981	1999
صاحب الطريقة	Fischler & Bolles	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
النوع	Robust linear regression	Robust multivariate location-scatter estimator
المصدر التأسيسي	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.
الأسماء البديلة	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
ذات صلة	5	4
الملخص	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.