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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kharin, Yuriy (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Temas:
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Robustness in Statistical Forecasting  |h [electronic resource] /  |c by Yuriy Kharin. 
250 |a 1st ed. 2013. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2013. 
300 |a XVI, 356 p. 47 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a 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. 
520 |a 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.      . 
650 0 |a Statistics . 
650 0 |a Probabilities. 
650 0 |a Engineering mathematics. 
650 0 |a Engineering-Data processing. 
650 1 4 |a Statistical Theory and Methods. 
650 2 4 |a Probability Theory. 
650 2 4 |a Statistics in Business, Management, Economics, Finance, Insurance. 
650 2 4 |a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 
650 2 4 |a Mathematical and Computational Engineering Applications. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319008417 
776 0 8 |i Printed edition:  |z 9783319008394 
776 0 8 |i Printed edition:  |z 9783319345680 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-319-00840-0  |z Texto Completo 
912 |a ZDB-2-SMA 
912 |a ZDB-2-SXMS 
950 |a Mathematics and Statistics (SpringerNature-11649) 
950 |a Mathematics and Statistics (R0) (SpringerNature-43713)