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The spectral analysis of time series [[electronic resource] /] / Lambert H. Koopmans
| The spectral analysis of time series [[electronic resource] /] / Lambert H. Koopmans |
| Autore | Koopmans Lambert Herman <1930-> |
| Edizione | [[2nd ed.].] |
| Pubbl/distr/stampa | San Diego, : Academic Press, c1995 |
| Descrizione fisica | 1 online resource (385 p.) |
| Disciplina |
519.5/5
519.55 |
| Collana | Probability and mathematical statistics |
| Soggetto topico |
Spectral theory (Mathematics)
Time-series analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-74938-9
9786611749385 0-08-054156-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 |
| Record Nr. | UNINA-9910458399803321 |
Koopmans Lambert Herman <1930->
|
||
| San Diego, : Academic Press, c1995 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The spectral analysis of time series [[electronic resource] /] / Lambert H. Koopmans
| The spectral analysis of time series [[electronic resource] /] / Lambert H. Koopmans |
| Autore | Koopmans Lambert Herman <1930-> |
| Edizione | [[2nd ed.].] |
| Pubbl/distr/stampa | San Diego, : Academic Press, c1995 |
| Descrizione fisica | 1 online resource (385 p.) |
| Disciplina |
519.5/5
519.55 |
| Collana | Probability and mathematical statistics |
| Soggetto topico |
Spectral theory (Mathematics)
Time-series analysis |
| ISBN |
1-281-74938-9
9786611749385 0-08-054156-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 |
| Record Nr. | UNINA-9910784639303321 |
|
Koopmans Lambert Herman <1930->
|
||
| San Diego, : Academic Press, c1995 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The spectral analysis of time series / / Lambert H. Koopmans
| The spectral analysis of time series / / Lambert H. Koopmans |
| Autore | Koopmans Lambert Herman <1930-> |
| Edizione | [[2nd ed.].] |
| Pubbl/distr/stampa | San Diego, : Academic Press, c1995 |
| Descrizione fisica | 1 online resource (385 p.) |
| Disciplina |
519.5/5
519.55 |
| Collana | Probability and mathematical statistics |
| Soggetto topico |
Spectral theory (Mathematics)
Time-series analysis |
| ISBN |
1-281-74938-9
9786611749385 0-08-054156-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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 |
| Record Nr. | UNINA-9910808852503321 |
|
Koopmans Lambert Herman <1930->
|
||
| San Diego, : Academic Press, c1995 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||