Robustness in statistical forecasting
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.), leading to actual forecast risks (mean square errors of predi...
Gespeichert in:
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| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
Cham :
Springer International Publishing
2013.
Cham : Springer Nature |
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Robustness in Statistical Forecasting, Texte imprimé, 9783319008417 • Robustness in Statistical Forecasting, Texte imprimé, 9783319345680 • Robustness in Statistical Forecasting, by Yuriy Kharin, Cham, Springer, 2013, 978-3-319-00839-4 |
| Zusammenfassung: | 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.), 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, offering solutions to the following topical problems: - developing mathematical models and descriptions of typical distortions in applied forecasting problems; - evaluating the robustness for traditional forecasting procedures under distortions; - obtaining the maximal distortion levels that allow the safe use of the traditional forecasting algorithms; - creating new robust forecasting procedures to arrive at risks that are less sensitive to definite distortion types. . |
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| Beschreibung: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783319008400 |
| Zugangseinschränkungen: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |