Comparar métodos

	Estimación MM para Regresión Robusta	Regresión RANSAC
Campo	Estadística	Estadística
Familia	Regression model	Regression model
Año de origen	1987	1981
Autor original	Victor J. Yohai	Fischler & Bolles
Tipo	Robust linear regression	Robust linear regression
Fuente seminal	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	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.
Alias	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu
Relacionados	5	5
Resumen	The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.	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.