পদ্ধতির তুলনা করুন

	MM-Estimation for Robust Regression	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1987	2019
প্রবর্তক	Victor J. Yohai	Wooldridge (textbook treatment); classical least squares
ধরন	Robust linear regression	Linear regression
মৌলিক উৎস	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	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
সারসংক্ষেপ	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).