Compară metode

	Modelul SARIMA Robust	Regresie Robustă
Domeniu	Econometrie	Statistică
Familie	Regression model	Regression model
Anul apariției	1979–2009	1964
Autorul original	Muler, Peña & Yohai (robust ARMA); earlier foundation by Denby & Martin (1979)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Tip	Robust time-series model	Regression with outlier resistance
Sursa seminală	Muler, N., Peña, D., & Yohai, V. J. (2009). Robust estimation for ARMA models. The Annals of Statistics, 37(2), 816–840.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Denumiri alternative	robust SARIMA, outlier-resistant SARIMA, robust seasonal ARIMA, M-estimator SARIMA	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Înrudite	4	6
Rezumat	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.