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The spectral analysis of time series /

Now Available in Paperback!

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Koopmans, Lambert H. (Lambert Herman), 1930-2011 (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Diego : Academic Press, [1995]
Edición:[2nd ed.].
Colección:Probability and mathematical statistics ; v. 22.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Koopmans, Lambert H.  |q (Lambert Herman),  |d 1930-2011,  |e author. 
245 1 4 |a The spectral analysis of time series /  |c Lambert H. Koopmans. 
250 |a [2nd ed.]. 
264 1 |a San Diego :  |b Academic Press,  |c [1995] 
264 4 |c ©1995 
300 |a 1 online resource (xvi, 366 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Probability and mathematical statistics ;  |v v. 22 
504 |a Includes bibliographical references (pages 354-358) and index. 
588 0 |a Print version record. 
520 |a Now Available in Paperback! 
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. 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
650 0 |a Time-series analysis. 
650 0 |a Spectral theory (Mathematics) 
650 6 |a Série chronologique. 
650 6 |a Spectre (Mathématiques) 
650 7 |a MATHEMATICS  |x Functional Analysis.  |2 bisacsh 
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650 7 |a Time-series analysis.  |2 fast  |0 (OCoLC)fst01151190 
653 0 |a Physics  |a Mathematics 
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830 0 |a Probability and mathematical statistics ;  |v v. 22. 
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