Vertaile menetelmiä
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| Robustin aikasarja-analyysi× | Sn ja Qn – robustit skaalaestimaattorit× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2019 | 1993 |
| Kehittäjä≠ | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition | Rousseeuw & Croux |
| Tyyppi≠ | Robust time series model (AR / MA / ARIMA) | Robust scale estimator |
| 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 | Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283. DOI ↗ |
| Rinnakkaisnimet≠ | robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi | Sn estimator, Qn estimator, Rousseeuw-Croux scale estimators, robust scale estimation |
| 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). | Sn and Qn are robust estimators of scale (spread) proposed by Rousseeuw and Croux (1993) as alternatives to the median absolute deviation (MAD). Both attain a 50% breakdown point while delivering higher statistical efficiency than MAD, so they measure dispersion accurately even when the data contain outliers. |
| ScholarGateAineisto ↗ |
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