MARC Record from harvard_bibliographic_metadata

LEADER: 02649nam a22004935a 4500
001 013840940-4
005 20131206202033.0
008 121227s1993 xxu| s ||0| 0|eng d
020 $a9781468401981
020 $a9781468401981
020 $a9781468402001
024 7 $a10.1007/978-1-4684-0198-1$2doi
035 $a(Springer)9781468401981
040 $aSpringer
050 4 $aQA276-280
072 7 $aPBT$2bicssc
072 7 $aMAT029000$2bisacsh
082 04 $a519.5$223
100 1 $aReinsel, Gregory C.,$eauthor.
245 10 $aElements of Multivariate Time Series Analysis /$cby Gregory C. Reinsel.
264 1 $aNew York, NY :$bSpringer US,$c1993.
300 $aXIV, 263p. 11 illus.$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
520 $aThis book is concerned with the analysis of multivariate time series data. Such data might arise in business and economics, engineering, geophysical sciences, agriculture, and many other fields. The emphasis is on providing an account of the basic concepts and methods which are useful in analyzing such data, and includes a wide variety of examples drawn from many fields of application. The book presupposes a familiarity with univariate time series as might be gained from one semester of a graduate course, but it is otherwise self-contained. It covers the basic topics such as autocovariance matrices of stationary processes, vector ARMA models and their properties, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models. In addition, it presents some more advanced topics and techniques including reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate nonstationary unit root models and co-integration structure and state-space models and Kalman filtering techniques.
650 20 $aComputational intelligence.
650 20 $aEconomics.
650 10 $aStatistics.
650 0 $aStatistics.
650 0 $aChemistry$xMathematics.
650 0 $aPhysiology$xMathematics.
650 0 $aEngineering.
650 0 $aEconomics.
650 24 $aStatistics, general.
650 24 $aMathematical and Computational Biology.
650 24 $aMath. Applications in Chemistry.
650 24 $aPhysiological, Cellular and Medical Topics.
776 08 $iPrinted edition:$z9781468402001
830 0 $aSpringer Series in Statistics.
988 $a20131119
906 $0VEN