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Springer Series in Statistics: Elements of Multivariate Time Series Analysis
Vergriffenes Buch, derzeit bei uns nicht verfügbar.
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Springer Series in Statistics: Elements of Multivariate Time Series Analysis - Taschenbuch

2003, ISBN: 9780387406190

[ED: Taschenbuch / Paperback], [PU: Springer, Berlin], AUSFÜHRLICHERE BESCHREIBUNG: Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures. INHALT: 1. Vector Time Series and Model Representations.- 1.1 Stationary Multivariate Time Series and Their Properties.- 1.1.1 Covariance and Correlation Matrices for a Stationary Vector Process.- 1.1.2 Some Spectral Characteristics for a Stationary Vector Process.- 1.1.3 Some Relations for Linear Filtering of a Stationary Vector Process.- 1.2 Linear Model Representations for a Stationary Vector Process.- 1.2.1 Infinite Moving Average (Wold) Representation of a Stationary Vector Process.- 1.2.2 Vector Autoregressive Moving Average (ARMA) Model Representations.- A1 Appendix: Review of Multivariate Normal Distribution and Related Topics.- A l. l Review of Some Basic Matrix Theory Results.- A l. 2 Vec Operator and Kronecker Products of Matrices.- A l. 3 Expected Values and Covariance Matrices of Random Vectors.- A1.4 The Multivariate Normal Distribution.- A1.5 Some Basic Results on Stochastic Convergence.- 2. Vector ARMA Time Series Models and Forecasting.- 2.1 Vector Moving Average Models.- 2.1.1 Invertibility of the Vector Moving Average Model.- 2.1.2 Covariance Matrices of the Vector Moving Average Model.- 2.1.3 Features of the Vector MA(1) Model.- 2.1.4 Model Structure for Subset of Components in the Vector MA Model.- 2.2 Vector Autoregressive Models.- 2.2.1 Stationarity of the Vector Autoregressive Model.- 2.2.2 Yule-Walker Relations for Covariance Matrices of a Vector AR Process.- 2.2.3 Covariance Features of the Vector AR(1) Model.- 2.2.4 Univariate Model Structure Implied by Vector AR Model.- 2.3 Vector Mixed Autoregressive Moving Average Models.- 2.3.1 Stationarity and Invertibility of the Vector ARMA Model.- 2.3.2 Relations for the Covariance Matrices of the Vector ARMA Model.- 2.3.3 Some Features of the Vector ARMA(1,1) Model.- 2.3.4 Consideration of Parameter Identifiability for Vector ARMA Models.- 2.3.5 Further Aspects of Nonuniqueness of Vector ARMA Model Representations.- 2.4 Nonstationary Vector ARMA Models.- 2.4.1 Vector ARIMA Models for Nonstationary Processes.- 2.4.2 Cointegration in Nonstationary Vector Processes.- 2.4.3 The Vector IMA(1,1) Process or Exponential Smoothing Model.- 2.5 Prediction for Vector ARMA Models.- 2.5.1 Minimum Mean Squared Error Prediction.- 2.5.2 Forecasting for Vector ARMA Processes and Covariance Matrices of Forecast Errors.- 2.5.3 Computation of Forecasts for Vector ARMA Processes.- 2.5.4 Some Examples of Forecast Functions for Vector ARMA Models.- 2.6 State-Space Form of the Vector ARMA Model.- A2 Appendix: Methods for Obtaining Autoregressive and Moving Average Parameters from Covariance Matrices.- A2.1 Iterative Algorithm for Factorization of Moving Average Spectral Density Matrix in Terms of Covariance Matrices.- A2.2 Autoregressive and Moving Average Parameter Matrices in Terms of Covariance Matrices for the Vector ARMA Model.- A2.3 Evaluation of Covariance Matrices in Terms of the AR and MA Parameters for the Vector ARMA Model.- 3. Canonical Structure of Vector ARMA Models.- 3.1 Consideration of Kronecker Structure for Vector ARMA Models.- 3.1.1 Kronecker Indices and McMillan Degree of Vector ARMA Process.- 3.1.2 Echelon Form Structure of Vector ARMA Model Implied by Kronecker Indices.- 3.1.3 Reduced-Rank Form of Vector ARMA Model Implied by Kronecker Indices.- 3.2 Canonical Correlation Structure for ARMA Time Series.- 3.2.1 Review of Canonical Correlations in Multivariate Analysis.- 3.2.2 Canonical Correlations for Vector ARMA Processes.- 3.2.3 Relation to Scalar Component Model Structure.- 3.3 Partial Autoregressive and Partial Correlation Matrices.- 3.3.1 Vector Autoregressive Model Approximations and Partial Autoregression Matrices.- 3.3.2 Recursive Fitting of Vector AR Model Approximations.- 3.3.3 Partial Cross-Correlation Matrices for a Stationary Vector Process.- 3.3.4 Partial Canonical Correlations for a Stationary Vector Process.- 4. Initial Model Building and Least Squares Estimation for Vector AR Models.- 4.1 Sample Cross-Covariance and Correlation Matrices and Their P, [SC: 5.50], Neuware, gewerbliches Angebot, 24,5 cm, [GW: 520g]

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Springer Series in Statistics: Elements of Multivariate Time Series Analysis
Vergriffenes Buch, derzeit bei uns nicht verfügbar.
(*)
Springer Series in Statistics: Elements of Multivariate Time Series Analysis - Taschenbuch

2003, ISBN: 9780387406190

[ED: Taschenbuch / Paperback], [PU: Springer, Berlin], AUSFÜHRLICHERE BESCHREIBUNG: Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures. INHALT: 1. Vector Time Series and Model Representations.- 1.1 Stationary Multivariate Time Series and Their Properties.- 1.1.1 Covariance and Correlation Matrices for a Stationary Vector Process.- 1.1.2 Some Spectral Characteristics for a Stationary Vector Process.- 1.1.3 Some Relations for Linear Filtering of a Stationary Vector Process.- 1.2 Linear Model Representations for a Stationary Vector Process.- 1.2.1 Infinite Moving Average (Wold) Representation of a Stationary Vector Process.- 1.2.2 Vector Autoregressive Moving Average (ARMA) Model Representations.- A1 Appendix: Review of Multivariate Normal Distribution and Related Topics.- A l. l Review of Some Basic Matrix Theory Results.- A l. 2 Vec Operator and Kronecker Products of Matrices.- A l. 3 Expected Values and Covariance Matrices of Random Vectors.- A1.4 The Multivariate Normal Distribution.- A1.5 Some Basic Results on Stochastic Convergence.- 2. Vector ARMA Time Series Models and Forecasting.- 2.1 Vector Moving Average Models.- 2.1.1 Invertibility of the Vector Moving Average Model.- 2.1.2 Covariance Matrices of the Vector Moving Average Model.- 2.1.3 Features of the Vector MA(1) Model.- 2.1.4 Model Structure for Subset of Components in the Vector MA Model.- 2.2 Vector Autoregressive Models.- 2.2.1 Stationarity of the Vector Autoregressive Model.- 2.2.2 Yule-Walker Relations for Covariance Matrices of a Vector AR Process.- 2.2.3 Covariance Features of the Vector AR(1) Model.- 2.2.4 Univariate Model Structure Implied by Vector AR Model.- 2.3 Vector Mixed Autoregressive Moving Average Models.- 2.3.1 Stationarity and Invertibility of the Vector ARMA Model.- 2.3.2 Relations for the Covariance Matrices of the Vector ARMA Model.- 2.3.3 Some Features of the Vector ARMA(1,1) Model.- 2.3.4 Consideration of Parameter Identifiability for Vector ARMA Models.- 2.3.5 Further Aspects of Nonuniqueness of Vector ARMA Model Representations.- 2.4 Nonstationary Vector ARMA Models.- 2.4.1 Vector ARIMA Models for Nonstationary Processes.- 2.4.2 Cointegration in Nonstationary Vector Processes.- 2.4.3 The Vector IMA(1,1) Process or Exponential Smoothing Model.- 2.5 Prediction for Vector ARMA Models.- 2.5.1 Minimum Mean Squared Error Prediction.- 2.5.2 Forecasting for Vector ARMA Processes and Covariance Matrices of Forecast Errors.- 2.5.3 Computation of Forecasts for Vector ARMA Processes.- 2.5.4 Some Examples of Forecast Functions for Vector ARMA Models.- 2.6 State-Space Form of the Vector ARMA Model.- A2 Appendix: Methods for Obtaining Autoregressive and Moving Average Parameters from Covariance Matrices.- A2.1 Iterative Algorithm for Factorization of Moving Average Spectral Density Matrix in Terms of Covariance Matrices.- A2.2 Autoregressive and Moving Average Parameter Matrices in Terms of Covariance Matrices for the Vector ARMA Model.- A2.3 Evaluation of Covariance Matrices in Terms of the AR and MA Parameters for the Vector ARMA Model.- 3. Canonical Structure of Vector ARMA Models.- 3.1 Consideration of Kronecker Structure for Vector ARMA Models.- 3.1.1 Kronecker Indices and McMillan Degree of Vector ARMA Process.- 3.1.2 Echelon Form Structure of Vector ARMA Model Implied by Kronecker Indices.- 3.1.3 Reduced-Rank Form of Vector ARMA Model Implied by Kronecker Indices.- 3.2 Canonical Correlation Structure for ARMA Time Series.- 3.2.1 Review of Canonical Correlations in Multivariate Analysis.- 3.2.2 Canonical Correlations for Vector ARMA Processes.- 3.2.3 Relation to Scalar Component Model Structure.- 3.3 Partial Autoregressive and Partial Correlation Matrices.- 3.3.1 Vector Autoregressive Model Approximations and Partial Autoregression Matrices.- 3.3.2 Recursive Fitting of Vector AR Model Approximations.- 3.3.3 Partial Cross-Correlation Matrices for a Stationary Vector Process.- 3.3.4 Partial Canonical Correlations for a Stationary Vector Process.- 4. Initial Model Building and Least Squares Estimation for Vector AR Models.- 4.1 Sample Cross-Covariance and Correlation Matrices and Their P, [SC: 0.00], Neuware, gewerbliches Angebot, 24,5 cm, [GW: 520g]

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Elements Of Multivariate Time Series Analysis
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Elements Of Multivariate Time Series Analysis - neues Buch

ISBN: 9780387406190

ID: 5932848

Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models. Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures. Books, Science and Geography~~Mathematics~~Probability & Statistics, Elements Of Multivariate Time Series Analysis~~Book~~9780387406190~~Gregory C. Reinsel, , , , , , , , , ,, [PU: Springer]

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Elements of Multivariate Time Series Analysis - Gregory C. Reinsel
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Gregory C. Reinsel:
Elements of Multivariate Time Series Analysis - neues Buch

ISBN: 9780387406190

ID: 978038740619

Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures. Gregory C. Reinsel, Books, Science and Nature, Elements of Multivariate Time Series Analysis Books>Science and Nature, Springer New York

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Reinsel, Gregory C.:
Elements of Multivariate Time Series Analysis - neues Buch

2003, ISBN: 0387406190

ID: A3269217

Nachdr. 2003 Kartoniert / Broschiert Multivariat, Analyse / Zeitreihenanalyse, Zeitreihenanalyse ( Zeitreihe ), Zeitreihe - Zeitreihenanalyse, MATHEMATICS / Probability & Statistics / Multivariate Analysis, mit Schutzumschlag neu, [PU:Springer-Verlag GmbH]

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Elements of Multivariate Time Series Analysis
Autor:

Reinsel, Gregory C.

Titel:

Elements of Multivariate Time Series Analysis

ISBN-Nummer:

0387406190

Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures.

Detailangaben zum Buch - Elements of Multivariate Time Series Analysis


EAN (ISBN-13): 9780387406190
ISBN (ISBN-10): 0387406190
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2003
Herausgeber: Springer-Verlag GmbH
357 Seiten
Gewicht: 0,526 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 10.07.2007 21:06:02
Buch zuletzt gefunden am 28.11.2016 11:10:57
ISBN/EAN: 0387406190

ISBN - alternative Schreibweisen:
0-387-40619-0, 978-0-387-40619-0

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