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Time Series Analysis and Its Applications (4th Edition)

With R Examples

Author(s):
Publisher:

Springer

Pages: 562
Further Actions:

Recommend to library

AVAILABLE FORMATS

Paperback - 9783319524511

19 April 2017

$99.99

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Ebook - 9783319524528

25 April 2017

$79.99

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The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using...

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The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty.

The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods.

This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.

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Student-tested and improved

Accessible and complete treatment of modern time series analysis 

Promotes understanding of theoretical concepts by bringing them into a more practical context 

Comprehensive appendices covering the necessities of understanding the mathematics of time series analysis

Instructor's Manual available for adopters

New to this edition:

Introductions to each chapter replaced with one-page abstracts

All graphics and plots redone and made uniform in style

Bayesian section completely rewritten, covering linear Gaussian state space models only

R code for each example provided directly in the text for ease of data analysis replication

Expanded appendices with tutorials containing basic R and R time series commands

Data sets and additional R scripts available for download on Springer.com

Internal online links to every reference (equations, examples, chapters, etc.) 


1. Characteristics of Time Series
2. Time Series Regression and Exploratory Data Analysis
3. ARIMA Models
4. Spectral Analysis and Filtering
5. Additional Time Domain Topics
6. State-Space Models
7. Statistical Methods in the Frequency Domain
8. Appendix A: Large Sample Theory.- Appendix B: Time Domain Theory.- Appendix C: Spectral Domain Theory.- Appendix R: R Supplement.

“The authors have to be congratulated for their ability to describe in a book of less than 600 pages such a variety of topics and methods, together with scripts allowing the reproduction of the results, for so many real examples. It is a valuable contribution with a strong statistical orientation and a carefully designed pleasant typography.” (Anna Bartkowiak, ISCB News, iscb.info, Issue 65, June, 2018)
“The chapters are nicely structured, well presented and motivated. … it provides sufficient exercise questions making it easier for adoption as a graduate textbook. The book will be equally attractive to graduate students, practitioners, and researchers in the respective fields. … The book contributes stimulating and substantial knowledge for time series analysis for the benefit of a host of community and exhibits the use and practicality of the fabulous subject statistics.” (S. Ejaz Ahmed, Technometrics, Vol. 59 (4), November, 2017)
  
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Robert H. Shumway, PhD, is Professor Emeritus of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He won the 1986 American Statistical Association Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center Statistics Award; both awards were for joint papers on time series applications. He is also the author of a Prentice-Hall text on applied time series analysis and served as a Departmental Editor for the Journal of Forecasting and Associate Editor for the Journal of the American Statistical Association.

David S. Stoffer, PhD, is Professor of Statistics at the University...

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Robert H. Shumway, PhD, is Professor Emeritus of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He won the 1986 American Statistical Association Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center Statistics Award; both awards were for joint papers on time series applications. He is also the author of a Prentice-Hall text on applied time series analysis and served as a Departmental Editor for the Journal of Forecasting and Associate Editor for the Journal of the American Statistical Association.

David S. Stoffer, PhD, is Professor of Statistics at the University of Pittsburgh. He is a Fellow of the American Statistical Association and has made seminal contributions to the analysis of categorical time series. David won the 1989 American Statistical Association Award for Outstanding Statistical Application in a joint paper analyzing categorical time series arising in infant sleep-state cycling. He is currently a Departmental Editor of the Journal of Forecasting and an Associate Editor of the Annals of Statistical Mathematics. He has served as Program Director in the Division of Mathematical Sciences at the National Science Foundation and as Associate Editor for the Journal of the American Statistical Association.


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