000			01732nam a22002895i 4500
001			65717
005			20171220150128.0
010			_a978-3-319-00840-0 _dcompra
090			_a65717
100			_a20150401d2013 k||y0pory50 ba
101			_aeng
102			_aDE
200			_aRobustness in statistical forecasting _bDocumento electrónico _fYuriy Kharin
210			_aCham _cSpringer International Publishing _d2013
215			_aXVI, 356 p. _cil.
300			_aColocação: Online
303			_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			_aAnálise de séries cronológicas _910606
606			_aEstatística matemática _91379
680			_aQA280
700			_aKharin _bYuriy _921097
801			_aPT _gRPC
856			_uhttp://dx.doi.org/10.1007/978-3-319-00840-0
942			_2lcc _cF _n0