विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| डब्ल्यू-एस्टिमेटर रोबस्ट रिग्रेशन (वेल्श / टुकी बिस्क्वेयर)× | साधारण न्यूनतम वर्ग (OLS) समाश्रयण× | |
|---|---|---|
| क्षेत्र≠ | सांख्यिकी | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1974 | 2019 |
| प्रवर्तक≠ | Beaton & Tukey (bisquare weight); Welsch (Welsch weight) | Wooldridge (textbook treatment); classical least squares |
| प्रकार≠ | Robust regression (redescending M-estimator) | Linear regression |
| मौलिक स्रोत≠ | Beaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16(2), 147-185. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| उपनाम | Tukey bisquare M-estimator, Welsch M-estimator, redescending M-estimator, W-Tahmin Edici (Welsch / Tukey Bisquare) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| संबंधित≠ | 4 | 5 |
| सारांश≠ | The W-estimator is a family of robust M-estimator variants for linear regression that use the Tukey bisquare and Welsch weight functions, introduced in the line of work going back to Beaton and Tukey (1974). Because its weights fall rapidly toward zero as a residual grows, it resists outliers more strongly than the Huber M-estimator. | 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). |
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