পদ্ধতির তুলনা করুন

	Robust Ridge Regression	শক্তিশালী বহুচলক রৈখিক নির্ভরণ (Robust Multiple Linear Regression)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1991	1964–1980s
প্রবর্তক	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
ধরন	Regularized robust linear regression	Robust linear regression
মৌলিক উৎস	Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333.	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.
অপর নাম	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
সম্পর্কিত	5	6
সারসংক্ষেপ	Robust Ridge regression combines M-estimation with L2 (ridge) regularization to produce coefficient estimates that are simultaneously resistant to outliers and stable under multicollinearity. It minimizes a robust loss function (such as Huber's) penalized by the squared norm of the coefficient vector, downweighting influential observations while shrinking correlated predictors toward zero.	Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients.