MARC Record from harvard_bibliographic_metadata

LEADER: 03159nam a22004815a 4500
001 013839716-3
005 20131206201213.0
008 121227s1991 xxu| s ||0| 0|eng d
020 $a9781441903204
020 $a9781441903204
020 $a9781441903198
024 7 $a10.1007/978-1-4419-0320-4$2doi
035 $a(Springer)9781441903204
040 $aSpringer
050 4 $aQA276-280
072 7 $aPBT$2bicssc
072 7 $aMAT029000$2bisacsh
082 04 $a519.5$223
100 1 $aBrockwell, Peter J.,$eauthor.
245 10 $aTime Series: Theory and Methods /$cby Peter J. Brockwell, Richard A. Davis.
250 $aSecond Edition.
264 1 $aNew York, NY :$bSpringer New York :$bImprint: Springer,$c1991.
300 $aXVI, 579 p.$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 $aStationary Time Series -- Hilbert Spaces -- Stationary ARMA Processes -- The Spectral Representation of a Stationary Process -- Prediction of Stationary Processes -- Asymptotic Theory -- Estimation of the Mean and the Autocovariance Function -- Estimation for ARMA Models -- Model Building and Forecasting with ARIMA Processes -- Inference for the Spectrum of a Stationary Process -- Multivariate Time Series -- State-Space Models and the Kalman Recursions -- Further Topics -- Appendix: Data Sets -- Bibliography -- Index.
520 $aThis paperback edition is a reprint of the 1991 edition. Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed, but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It contains substantial chapters on multivariate series and state-space models (including applications of the Kalman recursions to missing-value problems) and shorter accounts of special topics including long-range dependence, infinite variance processes, and nonlinear models. Most of the programs used in the book are available in the modeling package ITSM2000, the student version of which can be downloaded from http://www.stat.colostate.edu/~pjbrock/student06.
650 20 $aEconometrics.
650 10 $aStatistics.
650 0 $aStatistics.
650 0 $aMathematical statistics.
650 0 $aEconomics$xStatistics.
650 0 $aEconometrics.
650 24 $aStatistical Theory and Methods.
650 24 $aStatistics for Business/Economics/Mathematical Finance/Insurance.
700 1 $aDavis, Richard A.,$eauthor.
776 08 $iPrinted edition:$z9781441903198
830 0 $aSpringer Series in Statistics.
988 $a20131119
906 $0VEN