পদ্ধতির তুলনা করুন

	হুবার রিগ্রেশন	MM-Estimation for Robust Regression	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1964	1987	2019
প্রবর্তক	Peter J. Huber	Victor J. Yohai	Wooldridge (textbook treatment); classical least squares
ধরন	Robust linear regression (M-estimation)	Robust linear regression	Linear regression
মৌলিক উৎস	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
অপর নাম	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
সম্পর্কিত	5	5	5
সারসংক্ষেপ	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.	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.	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).