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Table of contents
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CHAPTER 1 - Preliminaries
Pages 1-28 - Book chapterNo access
CHAPTER 2 - Models for Spectral Analysis—The Univariate Case
Pages 29-65 - Book chapterNo access
CHAPTER 3 - Sampling, Aliasing, and Discrete-Time Models
Pages 66-78 - Book chapterNo access
CHAPTER 4 - Linear Filters—General Properties with Applications to Continuous-Time Processes
Pages 79-118 - Book chapterNo access
CHAPTER 5 - Multivariate Spectral Models and Their Applications
Pages 119-164 - Book chapterNo access
CHAPTER 6 - Digital Filters
Pages 165-209 - Book chapterNo access
CHAPTER 7 - Finite Parameter Models, Linear Prediction, and Real-Time Filtering
Pages 210-256 - Book chapterNo access
CHAPTER 8 - The Distribution Theory of Spectral Estimates with Applications to Statistical Inference
Pages 257-293 - Book chapterNo access
CHAPTER 9 - Sampling Properties of Spectral Estimates, Experimental Design, and Spectral Computations
Pages 294-353 - Book chapterNo access
References
Pages 354-358 - Book chapterNo access
Index
Pages 359-366 - Book chapterNo access
Probability and Mathematical Statistics
Pages ibc1-ibc2
About the book
Description
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 unified framework for an otherwise scattered collection of seemingly isolated results.The books strength lies in its applicability to the needs of readers from many disciplines with varying backgrounds in mathematics. It provides a solid foundation in spectral analysis for fields that include statistics, signal process engineering, economics, geophysics, physics, and geology. Appendices provide details and proofs for those who are advanced in math. Theories are followed by examples and applications over a wide range of topics such as meteorology, seismology, and telecommunications.Topics covered include Hilbert spaces; univariate models for spectral analysis; multivariate spectral models; sampling, aliasing, and discrete-time models; real-time filtering; digital filters; linear filters; distribution theory; sampling properties ofspectral estimates; and linear prediction.
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 unified framework for an otherwise scattered collection of seemingly isolated results.The books strength lies in its applicability to the needs of readers from many disciplines with varying backgrounds in mathematics. It provides a solid foundation in spectral analysis for fields that include statistics, signal process engineering, economics, geophysics, physics, and geology. Appendices provide details and proofs for those who are advanced in math. Theories are followed by examples and applications over a wide range of topics such as meteorology, seismology, and telecommunications.Topics covered include Hilbert spaces; univariate models for spectral analysis; multivariate spectral models; sampling, aliasing, and discrete-time models; real-time filtering; digital filters; linear filters; distribution theory; sampling properties ofspectral estimates; and linear prediction.
Key Features
- Hilbert spaces
- univariate models for spectral analysis
- multivariate spectral models
- sampling, aliasing, and discrete-time models
- real-time filtering
- digital filters
- linear filters
- distribution theory
- sampling properties of spectral estimates
- linear prediction
- Hilbert spaces
- univariate models for spectral analysis
- multivariate spectral models
- sampling, aliasing, and discrete-time models
- real-time filtering
- digital filters
- linear filters
- distribution theory
- sampling properties of spectral estimates
- linear prediction
Details
ISBN
978-0-12-419251-5
Language
English
Published
1995
Copyright
Copyright © 1995 Elsevier Inc. All rights reserved
Imprint
Academic Press