Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Robustin liikkuvan keskiarvon (MA) malli× | ARIMA-malli (Autoregressiivinen integroitu liukuva keskiarvo)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1979–2009 | 1970 |
| Kehittäjä≠ | Denby & Martin (1979); Muler, Pena & Yohai (2009) | George Box and Gwilym Jenkins |
| Tyyppi≠ | Robust time series model | Time series forecasting model |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | robust MA, robust moving average, M-estimation MA, bounded-influence MA | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | 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 ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateAineisto ↗ |
|
|