Abstract
Problems of statistical forecasting of time series under distortions of hypothetical models are considered. A short review of the research in this field is presented. Mathematical descriptions of typical distortions are given. Robustness characteristics for the mean square forecast risk (the risk instability coefficient and the δ-admissible distortion level) are evaluated for time series under distorted regression and autoregression models. New robust forecasting statistics are constructed. The theoretical results are illustrated by numerical experiments.
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Acknowledgements
The Belarusian Statistical Association gratefully acknowledge Professor Ursula Gather for supporting of the International Conference “Computer Data Analysis and Modeling” in Minsk for many years by her invited lectures and work in the Program Committee. The author thanks anonymous Referees for the comments that have improved this Chapter.
The research was partially supported by the ISTC Project No. B-1910.
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Kharin, Y. (2013). Robustness in Statistical Forecasting. In: Becker, C., Fried, R., Kuhnt, S. (eds) Robustness and Complex Data Structures. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35494-6_14
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DOI: https://doi.org/10.1007/978-3-642-35494-6_14
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