MARC Record from harvard_bibliographic_metadata

LEADER: 03295nam a22004335a 4500
001 013841505-6
005 20131206202348.0
008 130604s1989 xxu| s ||0| 0|eng d
020 $a9781475793659
020 $a9781475793659
020 $a9781475793673
024 7 $a10.1007/978-1-4757-9365-9$2doi
035 $a(Springer)9781475793659
040 $aSpringer
050 4 $aQA276-280
072 7 $aPBT$2bicssc
072 7 $aMAT029000$2bisacsh
082 04 $a519.5$223
100 1 $aWest, Mike,$eauthor.
245 10 $aBayesian Forecasting and Dynamic Models /$cby Mike West, Jeff Harrison.
264 1 $aNew York, NY :$bSpringer New York :$bImprint: Springer,$c1989.
300 $aXXI, 704 p.$bonline resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file$bPDF$2rda
490 1 $aSpringer Series in Statistics,$x0172-7397
505 0 $aContents: Introduction -- Introduction to the DLM: The First-Order Polynomial Model -- Introduction to the DLM: The Dynamic Regression Model -- The Dynamic Linear Model -- Univariate Time Series DLM Theory -- Model Specification and Design -- Polynomial Trend Models -- Seasonal Models -- Regression, Transfer Function and Noise Models -- Illustrations and Extensions of Standard DLMS -- Intervention and Monitoring -- Multi-Process Models -- Non-Linear Dynamic Models -- Exponential Family Dynamic Models -- Multivariate Modelling and Forecasting -- Appendix: Distribution Theory and Linear Algebra -- Bibliography -- Author Index -- Subject Index.
520 $aThe principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. Much progress has been made with mathematical and statistical aspects of forecasting models and related techniques, and experience has been gained through application in a variety of areas in commercial and industrial, scientific and socio-economic fields. Indeed much of the technical development has been driven by the needs of forecasting practitioners. There now exists a relatively complete statistical and mathematical framework that is described and illustrated here for the first time in book form, presenting our view of this approach to modelling and forecasting. The book provides a self-contained text for advanced university students and research workers in business, economic and scientific disciplines, and forecasting practitioners. The material covers mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each chapter. In order that the ideas and techniques of Bayesian forecasting be accessible to students, research workers and practitioners alike, the book includes a number of examples and case studies involving real data, generously illustrated using computer generated graphs. These examples provide issues of modelling, data analysis and forecasting.
650 20 $aEconomics.
650 10 $aStatistics.
650 0 $aStatistics.
650 0 $aEconomics.
650 24 $aStatistics, general.
700 1 $aHarrison, Jeff,$eauthor.
776 08 $iPrinted edition:$z9781475793673
830 0 $aSpringer Series in Statistics.
988 $a20131119
906 $0VEN