000			03285nam a2200265 4500
005			20251008114200.0
008			251006b |||||||| |||| 00| 0 eng d
020			_a9783031705830
082			_a519.55 _bSHU
100			_aShumway, Robert H. _924863
245			_aTime series analysis and its applications: _bwith R examples
250			_a5th
260			_aCham _bSpringer _c2025
300			_axvii, 599 p.
365			_aEUR _b139.99
490			_aSpringer Texts in Statistics (STS)
500			_aTable of contents: Front Matter Pages I-XVII Download chapter PDF Characteristics of Time Series Robert H. Shumway, David S. Stoffer Pages 1-48 Time Series Regression and Exploratory Data Analysis Robert H. Shumway, David S. Stoffer Pages 49-83 ARIMA Models Robert H. Shumway, David S. Stoffer Pages 85-175 Spectral Analysis and Filtering Robert H. Shumway, David S. Stoffer Pages 177-265 Additional Time Domain Topics Robert H. Shumway, David S. Stoffer Pages 267-309 State-Space Models Robert H. Shumway, David S. Stoffer Pages 311-415 Statistical Methods in the Frequency Domain Robert H. Shumway, David S. Stoffer Pages 417-502 [https://link.springer.com/book/10.1007/978-3-031-70584-7]
520			_aThis 5th edition of this popular graduate textbook presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. It includes 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 R package ‘astsa’ has had major updates and the text will reflect those updates. In general, the graphics have been improved. New topics include random number generation, modeling and fitting predator-prey interactions, more emphasis on structural models, testing for linearity, discussion of EM algorithm is more extensive, Bayesian analysis of state space models and MCMC is more extensive (including new scripts in astsa), particle methods are introduced, stochastic volatility coverage is expanded, changepoint detection is introduced (new topic). 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, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example (https://link.springer.com/book/10.1007/978-3-031-70584-7)
650			_aApplied mathematics _915527
650			_aProbability
650			_aMathematical statistics
650			_aR (Statistical computing) _925382
700			_aStoffer, David S. _925379
942			_cBK _2ddc
999			_c10278 _d10278