مقایسهٔ روشها
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| تحلیل سری زمانی مقاوم× | تحلیل نقطه شکست× | |
|---|---|---|
| حوزه | آمار | آمار |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 2019 | 1983 |
| پدیدآور≠ | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition | Hampel (1971); Donoho & Huber (1983) |
| نوع≠ | Robust time series model (AR / MA / ARIMA) | Robustness diagnostic for estimators |
| منبع بنیادین≠ | Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley. ISBN: 978-1119214687 | Donoho, D. L. & Huber, P. J. (1983). The Notion of Breakdown Point. In A Festschrift for Erich L. Lehmann (pp. 157-184). Wadsworth. link ↗ |
| نامهای دیگر | robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi | breakdown point, finite-sample breakdown point, robustness breakdown analysis, Bozunma Noktası Analizi |
| مرتبط | 5 | 5 |
| خلاصه≠ | Robust Time Series Analysis fits autoregressive, moving-average, and ARIMA models to series that contain outliers or structural breaks, using M-estimation or MM-estimation instead of ordinary least squares so that a few anomalous observations do not distort the fit. It follows the robust statistics tradition consolidated in Maronna, Martin, Yohai and Salibián-Barrera (2019). | 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. |
| ScholarGateمجموعهداده ↗ |
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