Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Robust ARMA -malli× | Robustti autoregressiivinen malli× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi | 1986 | 1986 |
| Kehittäjä≠ | Martin & Yohai (1986); broader robust time series literature | Martin & Yohai (influential early work); broader robust time series literature |
| Tyyppi | Robust time series model | Robust time series model |
| Alkuperäislähde≠ | Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15(1), 1-9. link ↗ | Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818. DOI ↗ |
| Rinnakkaisnimet | robust ARMA, outlier-robust ARMA, M-estimator ARMA, resistant ARMA estimation | robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR |
| Liittyvät≠ | 5 | 6 |
| Tiivistelmä≠ | The Robust ARMA model extends the classical Autoregressive Moving Average framework by replacing the sensitive least-squares loss with outlier-resistant estimation methods — typically M-estimators or median-based approaches. This protects coefficient estimates and forecasts from being distorted by additive outliers, level shifts, or innovational outliers that are common in economic and financial time series. | The robust AR model fits an autoregressive time series specification using estimation methods — typically M-estimators or bounded-influence estimators — that resist distortion from outliers and heavy-tailed error distributions. Unlike OLS-based AR estimation, robust variants down-weight extreme observations so that a small number of contaminated data points cannot dominate the fitted dynamics. |
| ScholarGateAineisto ↗ |
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