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| ブレークダウン・ポイント分析× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | 統計学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1983 | 2019 |
| 提唱者≠ | Hampel (1971); Donoho & Huber (1983) | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Robustness diagnostic for estimators | Linear regression |
| 原典≠ | 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 |
| 別名 | breakdown point, finite-sample breakdown point, robustness breakdown analysis, Bozunma Noktası Analizi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連 | 5 | 5 |
| 概要≠ | 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). |
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