方法对比
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| 稳健时间序列分析× | Sn和Qn稳健尺度估计量× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2019 | 1993 |
| 提出者≠ | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition | Rousseeuw & Croux |
| 类型≠ | Robust time series model (AR / MA / ARIMA) | Robust scale estimator |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi | Sn estimator, Qn estimator, Rousseeuw-Croux scale estimators, robust scale estimation |
| 相关 | 5 | 5 |
| 摘要≠ | 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. |
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