A Course in Time Series Analysis

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9780471361640: A Course in Time Series Analysis

New statistical methods and future directions of research in time series

A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:

  • Contributions from eleven of the worldâ??s leading figures in time series
  • Shared balance between theory and application
  • Exercise series sets
  • Many real data examples
  • Consistent style and clear, common notation in all contributions
  • 60 helpful graphs and tables

    Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.

    An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

"Sinopsis" puede pertenecer a otra edición de este libro.

About the Author:

DANIEL PE?A, PhD, is Professor of Statistics, Universidad Carlos III de Madrid.

GEORGE C. TIAO, PhD, is W. Allen Wallis Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.

RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.

Review:

"This text demonstrate how to build time series models for univariate and multivariate time series data." (SciTech Book News, Vol. 25, No. 2, June 2001)

"...material is thoroughly and carefully presented...a very useful addition to any collection both for learning and reference." (Short Book Reviews, Vol. 21, No. 2, August 2001)

"From the preface: ?The book can be used as a principal text or a complementary text for courses in time series.?" (Mathematical Reviews, Issue 2001k)

"...an excellent complement...for a first graduate course in time series analysis...a nice addition to anyone?s time series library." (Technometrics, Vol. 43, No. 4, November 2001)

"If you are familiar with the basics...and need a compass to navigate the vast world of time series literature, then this book is certainly what you need to have around...presents seamlessly and coherently overviews of the current status of time series research and applications." (The American Statistician, Vol. 56, No. 1, February 2002)

"...an excellent source of introductory surveys of several timely topics in time series analysis..." (Statistical Papers, July 2002)

"...a nice compendium covering a lot of relevant material..." (Statistics & Decisions, Vol.20, No.4, 2002)

"Sobre este título" puede pertenecer a otra edición de este libro.

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Peña, Daniel
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ISBN 10: 047136164X ISBN 13: 9780471361640
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Descripción John Wiley Sons Inc, United States, 2000. Hardback. Estado de conservación: New. Language: English . Brand New Book. New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: * Contributions from eleven of the worlda s leading figures in time series * Shared balance between theory and application * Exercise series sets * Many real data examples * Consistent style and clear, common notation in all contributions *60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department. Nº de ref. de la librería AAH9780471361640

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Descripción John Wiley Sons Inc, United States, 2000. Hardback. Estado de conservación: New. New.. Language: English . Brand New Book. New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: * Contributions from eleven of the worlda s leading figures in time series * Shared balance between theory and application * Exercise series sets * Many real data examples * Consistent style and clear, common notation in all contributions *60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department. Nº de ref. de la librería AAH9780471361640

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