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...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | Livre numérique |
| اللغة: | Anglais |
| منشور في: |
Cham :
Springer International Publishing
2013.
Cham : Springer Nature |
| الوصول للمادة أونلاين: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| ملاحظة: |
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 |
جدول المحتويات:
- Preface
- Symbols and Abbreviations
- Introduction
- A Decision-Theoretic Approach to Forecasting
- Time Series Models of Statistical Forecasting
- Performance and Robustness Characteristics in Statistical Forecasting
- Forecasting under Regression Models of Time Series
- Robustness of Time Series Forecasting Based on Regression Models
- Optimality and Robustness of ARIMA Forecasting
- Optimality and Robustness of Vector Autoregression Forecasting under Missing Values
- Robustness of Multivariate Time Series Forecasting Based on Systems of Simultaneous Equations
- Forecasting of Discrete Time Series
- Index.