Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo Robusto de Media Móvil (MA)× | Modelo ARMA (Autoregresivo de Media Móvil)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1979–2009 | 1970 |
| Autor original≠ | Denby & Martin (1979); Muler, Pena & Yohai (2009) | George E. P. Box and Gwilym M. Jenkins |
| Tipo≠ | Robust time series model | Time series model |
| Fuente seminal≠ | Denby, L., & Martin, R. D. (1979). Robust estimation of the first-order autoregressive parameter. Journal of the American Statistical Association, 74(365), 140–146. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | robust MA, robust moving average, M-estimation MA, bounded-influence MA | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | The Robust MA model applies robust estimation — typically M-estimation or bounded-influence methods — to the Moving Average time series model. By replacing the ordinary least squares loss with a bounded loss function, it produces parameter estimates that are far less sensitive to outliers, additive noise spikes, or heavy-tailed error distributions than the classical Gaussian MA. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
| ScholarGateConjunto de datos ↗ |
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