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	मजबूत सामान्यीकृत न्यूनतम वर्ग (मजबूत GLS)	सामान्यीकृत न्यूनतम वर्ग (GLS)	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	अर्थमिति	सांख्यिकी	अर्थमिति
परिवार	Regression model	Regression model	Regression model
उद्भव वर्ष	1936 / 1980	1935	2019
प्रवर्तक	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Alexander Craig Aitken	Wooldridge (textbook treatment); classical least squares
प्रकार	Robust linear regression	Linear estimator	Linear regression
मौलिक स्रोत	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
उपनाम	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	GLS, Aitken estimator, EGLS, feasible GLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	5	3	5
सारांश	Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.	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).