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	Robust Linear Mixed-Effects Model	Robust Regression (रोबस्ट रिग्रेशन)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	2016	1964
प्रवर्तक	Richardson & Welsh (robust REML); Koller (robustlmm implementation)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
प्रकार	Robust linear mixed-effects model	Regression with outlier resistance
मौलिक स्रोत	Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
उपनाम	robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
संबंधित	5	6
सारांश	The robust mixed model is a linear mixed-effects model for panel and repeated-measures data that tolerates outliers and heavy-tailed errors. It replaces the usual likelihood with bounded-influence estimating equations, building on the robust restricted maximum likelihood of Richardson and Welsh (1995) and the robustlmm implementation of Koller (2016).	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.