Time Series Analysis and Its Applications - With R Examples - Grand Format

4th edition

Edition en anglais

Robert-H Shumway

,

David-S Stoffer

Note moyenne 
The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency... Lire la suite
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Résumé

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

Caractéristiques

  • Date de parution
    01/04/2017
  • Editeur
  • Collection
  • ISBN
    978-3-319-52451-1
  • EAN
    9783319524511
  • Format
    Grand Format
  • Présentation
    Broché
  • Nb. de pages
    562 pages
  • Poids
    1.09 Kg
  • Dimensions
    17,7 cm × 25,4 cm × 3,2 cm

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