विधियों की तुलना करें

	रैन्सैक रिग्रेशन	साधारण न्यूनतम वर्ग (OLS) समाश्रयण	क्वांटाइल रिग्रेशन	Robust Covariance (MCD)
क्षेत्र	सांख्यिकी	अर्थमिति	अर्थमिति	सांख्यिकी
परिवार	Regression model	Regression model	Regression model	Regression model
उद्भव वर्ष	1981	2019	1978	1999
प्रवर्तक	Fischler & Bolles	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
प्रकार	Robust linear regression	Linear regression	Conditional quantile regression	Robust multivariate location-scatter estimator
मौलिक स्रोत	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	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.
उपनाम	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
संबंधित	5	5	5	4
सारांश	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.