مقایسهٔ روش‌ها

	مدل خودرگرسیون مقاوم	مدل خودرگرسیون (AR)
حوزه	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model
سال پیدایش	1986	1970s (popularised 1976)
پدیدآور	Martin & Yohai (influential early work); broader robust time series literature	George E. P. Box and Gwilym M. Jenkins
نوع	Robust time series model	Time series model
منبع بنیادین	Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818.	Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043
نام‌های دیگر	robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR	AR model, AR(p) model, autoregression, AR process
مرتبط	6	6
خلاصه	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.