LEADER 03876nam 22007095 450 001 9910964628103321 005 20250325082615.0 010 $a9781441903204 010 $a1441903208 024 7 $a10.1007/978-1-4419-0320-4 035 $a(CKB)1000000000778576 035 $a(SSID)ssj0000327260 035 $a(PQKBManifestationID)11244054 035 $a(PQKBTitleCode)TC0000327260 035 $a(PQKBWorkID)10301428 035 $a(PQKB)11309782 035 $a(DE-He213)978-1-4419-0320-4 035 $a(MiAaPQ)EBC3070644 035 $a(PPN)177053011 035 $a(EXLCZ)991000000000778576 100 $a20121227d1991 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc 9$2rdamedia 183 $acr$2rdacarrierg 200 10$aTime Series: Theory and Methods /$fby Peter J. Brockwell, Richard A. Davis 205 $a2nd ed. 1991. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d1991. 215 $a1 online resource (XVI, 580 p.) 225 1 $aSpringer Series in Statistics,$x2197-568X 300 $aPreviously published in hardback: New York : Springer-Verlag, c1991. 311 08$a9780387974293 311 08$a0387974296 311 08$a9781441903198 311 08$a1441903194 320 $aIncludes bibliographical references (p. [561]-566) and index. 327 $a1 Stationary Time Series -- 2 Hilbert Spaces -- 3 Stationary ARMA Processes -- 4 The Spectral Representation of a Stationary Process -- 5 Prediction of Stationary Processes -- 6* Asymptotic Theory -- 7 Estimation of the Mean and the Autocovariance Function -- 8 Estimation for ARMA Models -- 9 Model Building and Forecasting with ARIMA Processes -- 10 Inference for the Spectrum of a Stationary Process -- 11 Multivariate Time Series -- 12 State-Space Models and the Kalman Recursions -- 13 Further Topics -- Appendix: Data Sets. 330 $aThis paperback edition is a reprint of the 1991 edition. Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed, but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It contains substantial chapters on multivariate series and state-space models (including applications of the Kalman recursions to missing-value problems) and shorter accounts of special topics including long-range dependence, infinite variance processes, and nonlinear models. Most of the programs used in the book are available in the modeling package ITSM2000, the student version of which can be downloaded from http://www.stat.colostate.edu/~pjbrock/student06. 410 0$aSpringer Series in Statistics,$x2197-568X 606 $aStatistics 606 $aEconometrics 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aEconometrics 606 $aStatistics in Business, Management, Economics, Finance, Insurance 615 0$aStatistics. 615 0$aEconometrics. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aEconometrics. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 676 $a516.362 700 $aBrockwell$b Peter J$0103203 701 $aDavis$b Richard A$0103204 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910964628103321 996 $aTime series$9438688 997 $aUNINA