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Record Nr. |
UNINA9910808852503321 |
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Autore |
Koopmans Lambert Herman <1930-> |
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Titolo |
The spectral analysis of time series / / Lambert H. Koopmans |
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Pubbl/distr/stampa |
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San Diego, : Academic Press, c1995 |
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ISBN |
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1-281-74938-9 |
9786611749385 |
0-08-054156-9 |
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Edizione |
[[2nd ed.].] |
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Descrizione fisica |
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1 online resource (385 p.) |
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Collana |
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Probability and mathematical statistics ; ; v. 22 |
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Disciplina |
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Soggetti |
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Spectral theory (Mathematics) |
Time-series analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. 354-358) and index. |
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Nota di contenuto |
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Front 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 |
2.5 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 |
Chapter 4. Linear Filters-General Properties with Applications to Continuous-Time Processes4.1 Introduction; 4.2 Linear Filters; 4.3 |
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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 |
5.5 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 |
Chapter 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 |
8.4 Distribution Theory for Multivariate Spectral Estimators with Applications to Statistical Inference |
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Sommario/riassunto |
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To 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 |
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