The spectral analysis of time series /:
Now Available in Paperback!
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
San Diego :
Academic Press,
[1995]
|
| Ausgabe: | [2nd ed.]. |
| Schriftenreihe: | Probability and mathematical statistics ;
v. 22. |
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | Now Available in Paperback! |
| Beschreibung: | 1 online resource (xvi, 366 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 354-358) and index. |
| ISBN: | 9780080541563 0080541569 0124192513 9780124192515 |
Internformat
MARC
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| 100 | 1 | |a Koopmans, Lambert H. |q (Lambert Herman), |d 1930-2011, |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjC76vTmcGYhHTHJ9gDkcK |0 http://id.loc.gov/authorities/names/n80090487 | |
| 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 | ||
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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. | |
| 650 | 0 | |a Time-series analysis. |0 http://id.loc.gov/authorities/subjects/sh85135430 | |
| 650 | 0 | |a Spectral theory (Mathematics) |0 http://id.loc.gov/authorities/subjects/sh85126408 | |
| 650 | 6 | |a Série chronologique. | |
| 650 | 6 | |a Spectre (Mathématiques) | |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-ocn298348071 |
|---|---|
| _version_ | 1816881684287586304 |
| adam_text | |
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| author | Koopmans, Lambert H. (Lambert Herman), 1930-2011 |
| author_GND | http://id.loc.gov/authorities/names/n80090487 |
| author_facet | Koopmans, Lambert H. (Lambert Herman), 1930-2011 |
| author_role | aut |
| author_sort | Koopmans, Lambert H. 1930-2011 |
| author_variant | l h k lh lhk |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QA280 |
| callnumber-raw | QA280 .K64 1995eb |
| callnumber-search | QA280 .K64 1995eb |
| callnumber-sort | QA 3280 K64 41995EB |
| callnumber-subject | QA - Mathematics |
| collection | ZDB-4-EBA |
| contents | 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. |
| ctrlnum | (OCoLC)298348071 |
| dewey-full | 515.7222 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 515 - Analysis |
| dewey-raw | 515.7222 |
| dewey-search | 515.7222 |
| dewey-sort | 3515.7222 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| edition | [2nd ed.]. |
| format | Electronic eBook |
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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. 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| id | ZDB-4-EBA-ocn298348071 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:37Z |
| institution | BVB |
| isbn | 9780080541563 0080541569 0124192513 9780124192515 |
| language | English |
| oclc_num | 298348071 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xvi, 366 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 1995 |
| publishDateSearch | 1995 |
| publishDateSort | 1995 |
| publisher | Academic Press, |
| record_format | marc |
| series | Probability and mathematical statistics ; |
| series2 | Probability and mathematical statistics ; |
| spelling | Koopmans, Lambert H. (Lambert Herman), 1930-2011, author. https://id.oclc.org/worldcat/entity/E39PCjC76vTmcGYhHTHJ9gDkcK http://id.loc.gov/authorities/names/n80090487 The spectral analysis of time series / Lambert H. Koopmans. [2nd ed.]. San Diego : Academic Press, [1995] ©1995 1 online resource (xvi, 366 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Probability and mathematical statistics ; v. 22 Includes bibliographical references (pages 354-358) and index. Print version record. Now Available in Paperback! 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. Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Spectral theory (Mathematics) http://id.loc.gov/authorities/subjects/sh85126408 Série chronologique. Spectre (Mathématiques) MATHEMATICS Functional Analysis. bisacsh Spectral theory (Mathematics) fast Time-series analysis fast Physics Mathematics dissertations. aat Academic theses fast Academic theses. lcgft http://id.loc.gov/authorities/genreForms/gf2014026039 Thèses et écrits académiques. rvmgf Print version: Koopmans, Lambert Herman, 1930- Spectral analysis of time series. [2nd ed.]. San Diego : Academic Press, ©1995 0124192513 9780124192515 (DLC) 95196482 (OCoLC)36767710 Probability and mathematical statistics ; v. 22. http://id.loc.gov/authorities/names/n42019721 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=230858 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9780124192515 Volltext |
| spellingShingle | Koopmans, Lambert H. (Lambert Herman), 1930-2011 The spectral analysis of time series / Probability and mathematical statistics ; 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. Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Spectral theory (Mathematics) http://id.loc.gov/authorities/subjects/sh85126408 Série chronologique. Spectre (Mathématiques) MATHEMATICS Functional Analysis. bisacsh Spectral theory (Mathematics) fast Time-series analysis fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85135430 http://id.loc.gov/authorities/subjects/sh85126408 http://id.loc.gov/authorities/genreForms/gf2014026039 |
| title | The spectral analysis of time series / |
| title_auth | The spectral analysis of time series / |
| title_exact_search | The spectral analysis of time series / |
| title_full | The spectral analysis of time series / Lambert H. Koopmans. |
| title_fullStr | The spectral analysis of time series / Lambert H. Koopmans. |
| title_full_unstemmed | The spectral analysis of time series / Lambert H. Koopmans. |
| title_short | The spectral analysis of time series / |
| title_sort | spectral analysis of time series |
| topic | Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Spectral theory (Mathematics) http://id.loc.gov/authorities/subjects/sh85126408 Série chronologique. Spectre (Mathématiques) MATHEMATICS Functional Analysis. bisacsh Spectral theory (Mathematics) fast Time-series analysis fast |
| topic_facet | Time-series analysis. Spectral theory (Mathematics) Série chronologique. Spectre (Mathématiques) MATHEMATICS Functional Analysis. Time-series analysis dissertations. Academic theses Academic theses. Thèses et écrits académiques. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=230858 https://www.sciencedirect.com/science/book/9780124192515 |
| work_keys_str_mv | AT koopmanslamberth thespectralanalysisoftimeseries AT koopmanslamberth spectralanalysisoftimeseries |