Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

3.5
ISBN-13: 9783319524511
ISBN-10: 3319524518
Edition: 4th ed. 2017
Author: Robert H. Shumway, David S. Stoffer
Publication date: 2017
Publisher: Springer
Format: Paperback 562 pages
Category: Statistics
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Book details

ISBN-13: 9783319524511
ISBN-10: 3319524518
Edition: 4th ed. 2017
Author: Robert H. Shumway, David S. Stoffer
Publication date: 2017
Publisher: Springer
Format: Paperback 562 pages
Category: Statistics

Summary

Acknowledged authors Robert H. Shumway , David S. Stoffer wrote Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) comprising 562 pages back in 2017. Textbook and eTextbook are published under ISBN 3319524518 and 9783319524511. Since then Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) textbook received total rating of 3.5 stars and was available to sell back to BooksRun online for the top buyback price of $ 31.16 or rent at the marketplace.

Description

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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