Titlebook: Robustness in Statistical Forecasting; Yuriy Kharin Book 2013 Springer International Publishing Switzerland 2013 62-02, 62M20, 62M10, 62G3

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Forecasting of Discrete Time Series,ete-valued time series models. The field of discrete statistics has remained relatively underdeveloped until the recent years, when rapid introduction of digital equipment stimulated the researchers to develop numerous discrete models and techniques. In this chapter, we discuss optimal forecasting s

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presents new robust forecasting procedures.Presentation of .Traditional procedures in the statistical forecasting of time series, which are proved to be optimal under the hypothetical model, are often not robust under relatively small distortions (misspecification, outliers, missing values, etc.),

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Book 2013st under relatively small distortions (misspecification, outliers, missing values, etc.), leading to actual forecast risks (mean square errors of prediction) that are much higher than the theoretical values. This monograph fills a gap in the literature on robustness in statistical forecasting, offer

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https://doi.org/10.1007/978-3-319-00840-062-02, 62M20, 62M10, 62G35, 62-07, 62F35, 62C20, 62P20; forecasting; model distortion; risk; robustness;

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