Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robusts autoregresīvais modelis× | Autoregresīvs modelis (AR)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 1970s (popularised 1976) |
| Autors≠ | Martin & Yohai (influential early work); broader robust time series literature | George E. P. Box and Gwilym M. Jenkins |
| Tips≠ | Robust time series model | Time series model |
| Pirmavots≠ | Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 |
| Citi nosaukumi | robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR | AR model, AR(p) model, autoregression, AR process |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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. | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. |
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