01026nam0 22002651i 450 UON0034621120231205104320.77120091118d1957 |0itac50 baspaES|||| |||||Revolucion y reaccion en el reinado de Carlos IVCarlos Corona Madrid, Ediciones Rialp1957434 p.19 cm.001UON001750622001 Biblioteca del pensamiento actual68SPAGNAStoriaSec. 18.-19.UONC045244FIESMadridUONL000218CORONACarlosUONV193592703022RialpUONV267230650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00346211SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI III STORIAEUR D C 0752 SI MR 67963 5 0752 Revolucion y reaccion en el reinado de Carlos IV1358800UNIOR04079nam 22007455 450 991043803050332120250410121714.03-319-00840-410.1007/978-3-319-00840-0(CKB)3710000000019087(EBL)1474340(OCoLC)861528599(SSID)ssj0001010524(PQKBManifestationID)11577423(PQKBTitleCode)TC0001010524(PQKBWorkID)10999468(PQKB)10896809(DE-He213)978-3-319-00840-0(MiAaPQ)EBC1474340(PPN)17242299X(EXLCZ)99371000000001908720130903d2013 u| 0engur|n|---|||||txtccrRobustness in Statistical Forecasting /by Yuriy Kharin1st ed. 2013.Cham :Springer International Publishing :Imprint: Springer,2013.1 online resource (369 p.)Description based upon print version of record.3-319-00839-0 Includes bibliographical references and index.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.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.      .StatisticsProbabilitiesStatisticsEngineering mathematicsEngineeringData processingStatistical Theory and MethodsProbability TheoryStatistics in Business, Management, Economics, Finance, InsuranceStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesMathematical and Computational Engineering ApplicationsStatistics.Probabilities.Statistics.Engineering mathematics.EngineeringData processing.Statistical Theory and Methods.Probability Theory.Statistics in Business, Management, Economics, Finance, Insurance.Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.Mathematical and Computational Engineering Applications.330.015195519519.2519.5Kharin Yuriyauthttp://id.loc.gov/vocabulary/relators/aut1060665BOOK9910438030503321Robustness in Statistical Forecasting2514807UNINA