Robustness in Statistical Forecasting

Robustness in Statistical Forecasting by Yuriy Kharin.

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...

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Bibliographic Details
Main Author: Kharin, Yuriy (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2013.
Edition:1st ed. 2013.
Series:Springer eBook Collection.
Subjects:
Statistics .
Probabilities.
Applied mathematics.
Engineering mathematics.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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