000			03313cam a22002775i 4500
003			KE-MeUCS
005			20211202152425.0
008			170410s2017 gw |||| o |||| 0|eng
020			_a9783319524511
040			_aDLC _cDLC _dKE-MeUCS
050			_aQA280.S5 2017
100	1		_aShumway, Robert H.
245	1	0	_aTime series analysis and its applications : _bwith R Examples / _cby Robert H. Shumway, David S. Stoffer.
250			_aFourth edition.
260			_aCham, Switzerland : _bSpringer, _c2017.
300			_axiii, 562 p.
490	1		_aSpringer Texts in Statistics,
504			_aIncludes bibliography and index.
520			_aThe 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. 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, et cetera).
650		0	_aStatistics. _944
650		0	_aR (computer program language)
650	1	4	_aStatistical Theory and Methods.
650	2	4	_aStatistics for Life Sciences, Medicine, Health Sciences.
700	1		_aStoffer, David S.
942			_2lcc _cBK _tRG
999			_c86929 _d86928