Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis Robusto de Series Temporales× | Análisis del punto de quiebre× | |
|---|---|---|
| Campo | Estadística | Estadística |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2019 | 1983 |
| Autor original≠ | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition | Hampel (1971); Donoho & Huber (1983) |
| Tipo≠ | Robust time series model (AR / MA / ARIMA) | Robustness diagnostic for estimators |
| Fuente seminal≠ | 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 ↗ |
| Alias | 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 |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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