LEADER 05424nam 2200685Ia 450 001 9910458399803321 005 20200520144314.0 010 $a1-281-74938-9 010 $a9786611749385 010 $a0-08-054156-9 035 $a(CKB)1000000000384925 035 $a(EBL)344689 035 $a(OCoLC)476160832 035 $a(SSID)ssj0000249601 035 $a(PQKBManifestationID)12076920 035 $a(PQKBTitleCode)TC0000249601 035 $a(PQKBWorkID)10205972 035 $a(PQKB)10677521 035 $a(MiAaPQ)EBC344689 035 $a(Au-PeEL)EBL344689 035 $a(CaPaEBR)ebr10244424 035 $a(CaONFJC)MIL174938 035 $a(EXLCZ)991000000000384925 100 $a19950622d1995 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe spectral analysis of time series$b[electronic resource] /$fLambert H. Koopmans 205 $a[2nd ed.]. 210 $aSan Diego $cAcademic Press$dc1995 215 $a1 online resource (385 p.) 225 1 $aProbability and mathematical statistics ;$vv. 22 300 $aDescription based upon print version of record. 311 $a0-12-419251-3 320 $aIncludes bibliographical references (p. 354-358) and index. 327 $aFront Cover; The Spectral Analysis of Time Series; Copyright Page; Contents; Preface; Acknowledgements; Preface to the Second Edition; Chapter 1. Preliminaries; 1.1 Introduction; 1.2 Time Series and Spectra; 1.3 Summary of Vector Space Geometry; 1.4 Some Probability Notations and Properties; Chapter 2. Models for Spectral Analysis-The Univariate Case; 2.1 Introduction; 2.2 The Wiener Theory of Spectral Analysis; 2.3 Stationary and Weakly Stationary Stochastic Processes; 2.4 The Spectral Representation for Weakly Stationary Stochastic Processes-A Special Case 327 $a2.5 The General Spectral Representation for Weakly Stationary Processes2.6 The Discrete and Continuous Components of the Process; 2.7 Physical Realization of the Different Kinds of Spectra; 2.8 The Real Spectral Representation; 2.9 Ergodicity and the Connection between the Wiener and Stationary Process Theories; 2.10 Statistical Estimation of the Autocovariance and the Mean Ergodic Theorem; Appendix to Chapter 2; Chapter 3. Sampling, Aliasing, and Discrete-Time Models; 3.1 Introduction; 3.2 Sampling and the Aliasing Problem; 3.3 The Spectral Model for Discrete-Time Series 327 $aChapter 4. Linear Filters-General Properties with Applications to Continuous-Time Processes4.1 Introduction; 4.2 Linear Filters; 4.3 Combining Linear Filters; 4.4 Inverting Linear Filters; 4.5 Nonstationary Processes Generated by Time Varying Linear Filters; Appendix to Chapter 4; Chapter 5. Multivariate Spectral Models and Their Applications; 5.1 Introduction; 5.2 The Spectrum of a Multivariate Time Series-Wiener Theory; 5.3 Multivariate Weakly Stationary Stochastic Processes; 5.4 Linear Filters for Multivariate Time Series 327 $a5.5 The Bivariate Spectral Parameters, Their Intepretations and Uses5.6 The Multivariate Spectral Parameters, Their Interpretations and Uses; Appendix to Chapter 5; Chapter 6. Digital Filters; 6.1 Introduction; 6.2 General Properties of Digital Filters; 6.3 The Effect of Finite Data Length; 6.4 Digital Filters with Finitely Many Nonzero Weights; 6.5 Digital Filters Obtained by Combining Simple Filters; 6.6 Filters with Gapped Weights and Results Concerning the Filtering of Series with Polynomial Trends; Appendix to Chapter 6 327 $aChapter 7. Finite Parameter Models, Linear Prediction, and Real-Time Filtering7.1 Introduction; 7.2 Moving Averages; 7.3 Autoregressive Processes; 7.4 The Linear Prediction Problem; 7.5 Mixed Autoregressive-Moving Average Processes and Recursive Prediction; 7.6 Linear Filtering in Real Time; Appendix to Chapter 7; Chapter 8. The Distribution Theory of Spectral Estimates with Applications to Statistical Inference; 8.1 Introduction; 8.2 Distribution of the Finite Fourier Transform and the Periodogram; 8.3 Distribution Theory for Univariate Spectral Estimators 327 $a8.4 Distribution Theory for Multivariate Spectral Estimators with Applications to Statistical Inference 330 $aTo tailor time series models to a particular physical problem and to follow the working of various techniques for processing and analyzing data, one must understand the basic theory of spectral (frequency domain) analysis of time series. This classic book provides an introduction to the techniques and theories of spectral analysis of time series. In a discursive style, and with minimal dependence on mathematics, the book presents the geometric structure of spectral analysis. This approach makes possible useful, intuitive interpretations of important time series parameters and provides a unifi 410 0$aProbability and mathematical statistics ;$vv. 22. 606 $aSpectral theory (Mathematics) 606 $aTime-series analysis 608 $aElectronic books. 615 0$aSpectral theory (Mathematics) 615 0$aTime-series analysis. 676 $a519.5/5 676 $a519.55 700 $aKoopmans$b Lambert Herman$f1930-$0252848 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910458399803321 996 $aSpectral analysis of time series$9439719 997 $aUNINA