000 03945nam a22004575i 4500
001 978-1-4419-7865-3
003 DE-He213
005 20140220083725.0
007 cr nn 008mamaa
008 101111s2011 xxu| s |||| 0|eng d
020 _a9781441978653
_9978-1-4419-7865-3
024 7 _a10.1007/978-1-4419-7865-3
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aShumway, Robert H.
_eauthor.
245 1 0 _aTime Series Analysis and Its Applications
_h[electronic resource] :
_bWith R Examples /
_cby Robert H. Shumway, David S. Stoffer.
264 1 _aNew York, NY :
_bSpringer New York,
_c2011.
300 _aXI, 596 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Texts in Statistics,
_x1431-875X
505 0 _aCharacteristics of time series -- Time series regression and exploratory data analysis -- ARIMA models -- Spectral analysis and filtering -- Additional time domain topics -- State-space models -- Statistical methods in the frequency domain.
520 _aTime Series Analysis and Its Applications 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 to be useful as a text 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, stochastic volatility, wavelets and Markov chain Monte Carlo integration methods.  The third edition includes a new section on testing for unit roots and the material on state-space modeling, ARMAX models, and regression with autocorrelated errors has been expanded. Also new to this edition is the enhanced use of the freeware statistical package R.  In particular, R code is now included in the text for nearly all of the numerical examples.  Data sets and additional R scripts are now provided in one file that may be downloaded via the World Wide Web.  This R supplement is a small compressed file that can be loaded easily into R making all the data sets and scripts available to the user with one simple command.  The website for the text includes the code used in each example so that the reader may simply copy-and-paste code directly into R.  Appendix R, which is new to this edition, provides a reference for the data sets and our R scripts that are used throughout the text. In addition, Appendix R includes a tutorial on basic R commands as well as an R time series tutorial.  
650 0 _aStatistics.
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
700 1 _aStoffer, David S.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441978646
830 0 _aSpringer Texts in Statistics,
_x1431-875X
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-7865-3
912 _aZDB-2-SMA
999 _c105877
_d105877