विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मजबूत सामान्यीकृत न्यूनतम वर्ग (मजबूत GLS)× | सशक्त ओएलएस (सशक्त मानक त्रुटियों के साथ ओएलएस)× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1936 / 1980 | 1980 |
| प्रवर्तक≠ | Aitken (GLS theory, 1936); White (robust covariance, 1980) | Halbert White |
| प्रकार≠ | Robust linear regression | Linear regression with robust inference |
| मौलिक स्रोत≠ | Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381 | White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. DOI ↗ |
| उपनाम | robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS | HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors |
| संबंधित≠ | 5 | 6 |
| 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. | Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations. |
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