Robustness in Statistical Forecasting

ISBN-13: 9783319345680 / Angielski / Miękka / 2016 / 356 str.

Yuriy Kharin

Opis

This book examines robustness of time series forecasting. It evaluates sensitivity of the forecast risks to distortions and presents new robust forecasting procedures.

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Recenzje

From the reviews:

"The book is intended for mathematicians, statisticians and software developers in applied mathematics, computer science, data analysis, and econometrics, among other topics. It is a good text for advanced undergraduate and postgraduate students of the mentioned disciplines." (Oscar Bustos, zbMATH, Vol. 1281, 2014)

Spis treści

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. ​

O autorze

Yuriy Kharin is Chairman of the Department of Mathematical Modeling & Data Analysis, Director of the Research Institute for Applied Problems of Mathematics & Informatics at the Belarusian State University. He completed his Ph.D. in Math. Sci. at the Tomsk State University in 1974 and his Dr. Sci. in Math. Sci. at the USSR Academy of Sciences in 1986. His research interests include mathematical and applied statistics, robust statistics, and statistical forecasting. He is founder and first President of the Belarusian Statistical Association (1998), Laureate of National Science Prize (2002), and a Correspondent Member of the National Academy of Sciences of Belarus (2004).

Blurb

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.