Vertaile menetelmiä
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| Robustin aikasarja-analyysi× | Hajoamispisteanalyysi× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2019 | 1983 |
| Kehittäjä≠ | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition | Hampel (1971); Donoho & Huber (1983) |
| Tyyppi≠ | Robust time series model (AR / MA / ARIMA) | Robustness diagnostic for estimators |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | 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 |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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