Vertaile menetelmiä
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| Mediaanin absoluuttisen poikkeaman (MAD) estimointi× | Robustin aikasarja-analyysi× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1974 | 2019 |
| Kehittäjä≠ | Hampel (influence-curve treatment); classical robust statistics | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition |
| Tyyppi≠ | Robust scale estimator | Robust time series model (AR / MA / ARIMA) |
| Alkuperäislähde≠ | Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393. DOI ↗ | 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 |
| Rinnakkaisnimet | median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini | robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result. | 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). |
| ScholarGateAineisto ↗ |
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