LEADER 04445nam 22007215 450 001 9910438030503321 005 20220413190446.0 010 $a3-319-00840-4 024 7 $a10.1007/978-3-319-00840-0 035 $a(CKB)3710000000019087 035 $a(EBL)1474340 035 $a(OCoLC)861528599 035 $a(SSID)ssj0001010524 035 $a(PQKBManifestationID)11577423 035 $a(PQKBTitleCode)TC0001010524 035 $a(PQKBWorkID)10999468 035 $a(PQKB)10896809 035 $a(DE-He213)978-3-319-00840-0 035 $a(MiAaPQ)EBC1474340 035 $a(PPN)17242299X 035 $a(EXLCZ)993710000000019087 100 $a20130903d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRobustness in statistical forecasting$b[electronic resource] /$fby Yuriy Kharin 205 $a1st ed. 2013. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2013. 215 $a1 online resource (369 p.) 300 $aDescription based upon print version of record. 311 $a3-319-00839-0 320 $aIncludes bibliographical references and index. 327 $aPreface -- 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. 330 $aTraditional 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.      . 606 $aStatistics  606 $aProbabilities 606 $aApplied mathematics 606 $aEngineering mathematics 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 606 $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006 615 0$aStatistics . 615 0$aProbabilities. 615 0$aApplied mathematics. 615 0$aEngineering mathematics. 615 14$aStatistical Theory and Methods. 615 24$aProbability Theory and Stochastic Processes. 615 24$aStatistics for Business, Management, Economics, Finance, Insurance. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aMathematical and Computational Engineering. 676 $a330.015195 676 $a519 676 $a519.2 676 $a519.5 700 $aKharin$b Yuriy$4aut$4http://id.loc.gov/vocabulary/relators/aut$01060665 906 $aBOOK 912 $a9910438030503321 996 $aRobustness in Statistical Forecasting$92514807 997 $aUNINA