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Robust Time Series Estimation via Weighted Likelihood

In: Developments in Robust Statistics

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  • C. Agostinelli

    (University of Venezia, Department of Statistics)

Abstract

Summary 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.

Suggested Citation

  • C. Agostinelli, 2003. "Robust Time Series Estimation via Weighted Likelihood," Springer Books, in: Rudolf Dutter & Peter Filzmoser & Ursula Gather & Peter J. Rousseeuw (ed.), Developments in Robust Statistics, pages 1-16, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-57338-5_1
    DOI: 10.1007/978-3-642-57338-5_1
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