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| Робастен SARIMA модел× | Робустна регресия× | |
|---|---|---|
| Област≠ | Иконометрия | Статистика |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1979–2009 | 1964 |
| Създател≠ | Muler, Peña & Yohai (robust ARMA); earlier foundation by Denby & Martin (1979) | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Тип≠ | Robust time-series model | Regression with outlier resistance |
| Основополагащ източник≠ | Muler, N., Peña, D., & Yohai, V. J. (2009). Robust estimation for ARMA models. The Annals of Statistics, 37(2), 816–840. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Други названия | robust SARIMA, outlier-resistant SARIMA, robust seasonal ARIMA, M-estimator SARIMA | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| Свързани≠ | 4 | 6 |
| Резюме≠ | Robust SARIMA extends the classical Seasonal ARIMA framework by replacing the standard least-squares criterion with a robust loss function — such as an M-estimator — so that outliers and heavy-tailed innovations in seasonal time series cannot distort parameter estimates or invalidate forecasts. | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. |
| ScholarGateНабор от данни ↗ |
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