000 03323nam a22004935i 4500
001 978-3-319-00840-0
003 DE-He213
005 20140220082839.0
007 cr nn 008mamaa
008 130903s2013 gw | s |||| 0|eng d
020 _a9783319008400
_9978-3-319-00840-0
024 7 _a10.1007/978-3-319-00840-0
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aKharin, Yuriy.
_eauthor.
245 1 0 _aRobustness in Statistical Forecasting
_h[electronic resource] /
_cby Yuriy Kharin.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXVI, 356 p. 47 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.      
650 0 _aStatistics.
650 0 _aDistribution (Probability theory).
650 0 _aMathematical statistics.
650 0 _aEconomics
_xStatistics.
650 0 _aEngineering mathematics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319008394
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-00840-0
912 _aZDB-2-SMA
999 _c96483
_d96483