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- W163911624 abstract "In this paper we introduce a method for efficient and robust estimation of the unknown parameters of an autoregressive-moving average model based on weighted likelihood. Two types of outliers, i.e. additive and innovation, are taken into account without knowing their number, position or intensity. A new procedure is used to classify the outliers and to bound the impact of additive outliers in order to improve the breakdown point of the method. Two examples and a Monte Carlo simulation are presented." @default.
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- W163911624 date "2003-01-01" @default.
- W163911624 modified "2023-09-27" @default.
- W163911624 title "Robust Time Series Estimation via Weighted Likelihood" @default.
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- W163911624 doi "https://doi.org/10.1007/978-3-642-57338-5_1" @default.
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