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| Analiza punktu krytycznego× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|
| Dziedzina≠ | Statystyka | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1983 | 2019 |
| Twórca≠ | Hampel (1971); Donoho & Huber (1983) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Robustness diagnostic for estimators | Linear regression |
| Źródło pierwotne≠ | Donoho, D. L. & Huber, P. J. (1983). The Notion of Breakdown Point. In A Festschrift for Erich L. Lehmann (pp. 157-184). Wadsworth. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy | breakdown point, finite-sample breakdown point, robustness breakdown analysis, Bozunma Noktası Analizi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Breakdown point analysis quantifies the fraction of outliers an estimator can tolerate before it produces meaningless results. Formalised by Hampel (1971) and Donoho and Huber (1983), it is the standard tool for comparing the robustness of competing estimators. | 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). |
| ScholarGateZbiór danych ↗ |
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