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The spectral analysis of time series

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The spectral analysis of time series/ Lambert H. Koopmans.
作者:	Koopmans, Lambert Herman,
出版者:	San Diego :Academic Press, : c1995.,
面頁冊數:	1 online resource (xvi, 366 p.) :ill.
內容註:	Preliminaries: Time Series and Spectra. Summary of Vector Space Geometry. Some Probability Notations and Properties. Models for Spectral Analysis-The Univariate Case: The Wiener Theory of Spectral Analysis. Stationary and Weakly Stationary Stochastic Processes. The Spectral Representation for Weakly Stationary Stochastic Processes-A Special Case. The General Spectral Representation for Weakly Stationary Processes. The Discrete and Continuous Components of the Process. Physical Realizations of the Different Kinds of Spectra. The Real Spectral Representation. Ergodicity and the Connection Between the Wiener and Stationary Process Theories. Statistical Estimation of the Autocovariance and the Mean Ergodic Theorem. Sampling, Aliasing, and Discrete-Time Models: Sampling and the Aliasing Problem. The Spectral Model for Discrete-Time Series. Linear Filters-General Properties with Applications to Continuous-Time Processes: Linear Filters. Combining Linear Filters. Inverting Linear Filters. Nonstationary Processes Generated by Time Varying Linear Filters. Multivariate Spectral Models and Their Applications: The Spectrum of a Multivariate Time Series-Wiener Theory. Multivariate Weakly Stationary Stochastic Processes. Linear Filters for Multivariate Time Series. The Bivariate Spectral Parameters, Their Interpretations and Uses. The Multivariate Spectral Parameters, Their Interpretations and Uses. Digital Filters: General Properties of Digital Filters. The Effect of Finite Data Length. Digital Filters with Finitely Many Nonzero Weights. Digital Filters Obtained by Combining Simple Filters. Filters with Gapped Weights and Results Concerning the Filtering of Series with Polynomial Trends. Finite Parameter Models, Linear Prediction, and Real-Time Filtering: Moving Averages. Autoregressive Processes. The Linear Prediction Problem. Mixed Autoregressive-Moving Average Processes and Recursive Prediction. Linear Filtering in Real Time The Distribution Theory of Spectral Estimates with Applications to Statistical Inference: Distribution of the Finite Fourier Transform and the Periodogram. Distribution Theory for Univariate Spectral Estimators. Distribution Theory for Multivariate Spectral Estimators with Applications to Statistical Inference. Sampling Properties of Spectral Estimates, Experimental Design, and Spectral Computations: Properties of Spectral Estimators and the Selection of Spectral Windows. Experimental Design. Methods for ComputingSpectral Estimators. Data Processing Problems and Techniques. References. Index.
標題:	Time-series analysis. -
電子資源:	http://www.sciencedirect.com/science/book/9780124192515
ISBN:	9780124192515

The spectral analysis of time series
Koopmans, Lambert Herman,1930-{me_controlnum}

The spectral analysis of time series[electronic resource] /Lambert H. Koopmans. - [2nd ed.]. - San Diego :Academic Press,c1995. - 1 online resource (xvi, 366 p.) :ill. - Probability and mathematical statistics ;v. 22. - Probability and mathematical statistics ;v. 22..

Includes bibliographical references (p. 354-358) and index.

Preliminaries: Time Series and Spectra. Summary of Vector Space Geometry. Some Probability Notations and Properties. Models for Spectral Analysis-The Univariate Case: The Wiener Theory of Spectral Analysis. Stationary and Weakly Stationary Stochastic Processes. The Spectral Representation for Weakly Stationary Stochastic Processes-A Special Case. The General Spectral Representation for Weakly Stationary Processes. The Discrete and Continuous Components of the Process. Physical Realizations of the Different Kinds of Spectra. The Real Spectral Representation. Ergodicity and the Connection Between the Wiener and Stationary Process Theories. Statistical Estimation of the Autocovariance and the Mean Ergodic Theorem. Sampling, Aliasing, and Discrete-Time Models: Sampling and the Aliasing Problem. The Spectral Model for Discrete-Time Series. Linear Filters-General Properties with Applications to Continuous-Time Processes: Linear Filters. Combining Linear Filters. Inverting Linear Filters. Nonstationary Processes Generated by Time Varying Linear Filters. Multivariate Spectral Models and Their Applications: The Spectrum of a Multivariate Time Series-Wiener Theory. Multivariate Weakly Stationary Stochastic Processes. Linear Filters for Multivariate Time Series. The Bivariate Spectral Parameters, Their Interpretations and Uses. The Multivariate Spectral Parameters, Their Interpretations and Uses. Digital Filters: General Properties of Digital Filters. The Effect of Finite Data Length. Digital Filters with Finitely Many Nonzero Weights. Digital Filters Obtained by Combining Simple Filters. Filters with Gapped Weights and Results Concerning the Filtering of Series with Polynomial Trends. Finite Parameter Models, Linear Prediction, and Real-Time Filtering: Moving Averages. Autoregressive Processes. The Linear Prediction Problem. Mixed Autoregressive-Moving Average Processes and Recursive Prediction. Linear Filtering in Real Time The Distribution Theory of Spectral Estimates with Applications to Statistical Inference: Distribution of the Finite Fourier Transform and the Periodogram. Distribution Theory for Univariate Spectral Estimators. Distribution Theory for Multivariate Spectral Estimators with Applications to Statistical Inference. Sampling Properties of Spectral Estimates, Experimental Design, and Spectral Computations: Properties of Spectral Estimators and the Selection of Spectral Windows. Experimental Design. Methods for ComputingSpectral Estimators. Data Processing Problems and Techniques. References. Index.

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.

ISBN: 9780124192515

Source: 81678:81678Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

532530
Time-series analysis.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA280 / .K64 1995eb

Dewey Class. No.: 515.7222

The spectral analysis of time series
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100 1 $a Koopmans, Lambert Herman, $d 1930-{me_controlnum} $3 1458592
245 1 4 $a The spectral analysis of time series $h [electronic resource] / $c Lambert H. Koopmans.
250 $a [2nd ed.].
260 $a San Diego : $b Academic Press, $c c1995.
300 $a 1 online resource (xvi, 366 p.) : $b ill.
490 1 $a Probability and mathematical statistics ; $v v. 22
504 $a Includes bibliographical references (p. 354-358) and index.
505 0 $a Preliminaries: Time Series and Spectra. Summary of Vector Space Geometry. Some Probability Notations and Properties. Models for Spectral Analysis-The Univariate Case: The Wiener Theory of Spectral Analysis. Stationary and Weakly Stationary Stochastic Processes. The Spectral Representation for Weakly Stationary Stochastic Processes-A Special Case. The General Spectral Representation for Weakly Stationary Processes. The Discrete and Continuous Components of the Process. Physical Realizations of the Different Kinds of Spectra. The Real Spectral Representation. Ergodicity and the Connection Between the Wiener and Stationary Process Theories. Statistical Estimation of the Autocovariance and the Mean Ergodic Theorem. Sampling, Aliasing, and Discrete-Time Models: Sampling and the Aliasing Problem. The Spectral Model for Discrete-Time Series. Linear Filters-General Properties with Applications to Continuous-Time Processes: Linear Filters. Combining Linear Filters. Inverting Linear Filters. Nonstationary Processes Generated by Time Varying Linear Filters. Multivariate Spectral Models and Their Applications: The Spectrum of a Multivariate Time Series-Wiener Theory. Multivariate Weakly Stationary Stochastic Processes. Linear Filters for Multivariate Time Series. The Bivariate Spectral Parameters, Their Interpretations and Uses. The Multivariate Spectral Parameters, Their Interpretations and Uses. Digital Filters: General Properties of Digital Filters. The Effect of Finite Data Length. Digital Filters with Finitely Many Nonzero Weights. Digital Filters Obtained by Combining Simple Filters. Filters with Gapped Weights and Results Concerning the Filtering of Series with Polynomial Trends. Finite Parameter Models, Linear Prediction, and Real-Time Filtering: Moving Averages. Autoregressive Processes. The Linear Prediction Problem. Mixed Autoregressive-Moving Average Processes and Recursive Prediction. Linear Filtering in Real Time The Distribution Theory of Spectral Estimates with Applications to Statistical Inference: Distribution of the Finite Fourier Transform and the Periodogram. Distribution Theory for Univariate Spectral Estimators. Distribution Theory for Multivariate Spectral Estimators with Applications to Statistical Inference. Sampling Properties of Spectral Estimates, Experimental Design, and Spectral Computations: Properties of Spectral Estimators and the Selection of Spectral Windows. Experimental Design. Methods for ComputingSpectral Estimators. Data Processing Problems and Techniques. References. Index.
520 $a 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.
588 $a Description based on print version record.
650 0 $a Time-series analysis. $3 532530
650 0 $a Spectral theory (Mathematics) $3 524915
655 4 $a Electronic books. $2 lcsh $3 542853
776 0 8 $i Print version: $a Koopmans, Lambert Herman, 1930- $t Spectral analysis of time series. $b [2nd ed.]. $d San Diego : Academic Press, c1995 $z 0124192513 $z 9780124192515 $w (DLC) 95196482 $w (OCoLC)36767710
830 0 $a Probability and mathematical statistics ; $v v. 22. $3 1458593
856 4 0 $3 ScienceDirect $u http://www.sciencedirect.com/science/book/9780124192515
994 $a C0 $b TEF