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	मजबूत सामान्यीकृत रैखिक मॉडल	Robust Regression (रोबस्ट रिग्रेशन)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	2001	1964
प्रवर्तक	Cantoni & Ronchetti	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
प्रकार	Robust regression model	Regression with outlier resistance
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. The Annals of Mathematical Statistics, 35(1), 73–101.
उपनाम	robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
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 regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.