Robust MA model
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.
Rekodi ya chanzo
Nukuu zimehamishwa kwa uhalisi kutoka kwa rekodi ya chanzo cha mbinu. Hakuna uthibitisho wa kiwango cha dai unaodokezwa kutoka kwao.
- 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 10.1080/01621459.1979.10481630
- Muler, N., Pena, D., & Yohai, V. J. (2009). Robust estimation for ARMA models. Annals of Statistics, 37(2), 816–840. · DOI 10.1214/07-AOS570
Madai yaliyotunzwa
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Mbinu zinazohusiana
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