विधियों की तुलना करें

	मजबूत सामान्यीकृत रैखिक मॉडल	मजबूत बहुरेखीय प्रतिगमन
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	2001	1964–1980s
प्रवर्तक	Cantoni & Ronchetti	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
प्रकार	Robust regression model	Robust linear regression
मौलिक स्रोत	Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.
उपनाम	robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
संबंधित	5	6
सारांश	A Robust Generalized Linear Model fits the standard GLM family — linear, logistic, Poisson, and others — using M-type estimating equations that down-weight outlying or influential observations. The result is coefficient estimates and standard errors that remain stable even when a minority of data points deviate sharply from the assumed distribution.	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.