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008			150903s2011 xxu| o |||| 0|eng d
020			_a9781441978653 _99781441978653
024	7		_a10.1007/9781441978653 _2doi
035			_avtls000339086
039		9	_a201509030836 _bVLOAD _c201404300354 _dVLOAD _y201402060927 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aShumway, Robert H. _eautor _9301402
245	1	0	_aTime Series Analysis and Its Applications : _bWith R Examples / _cby Robert H. Shumway, David S. Stoffer.
264		1	_aNew York, NY : _bSpringer New York, _c2011.
300			_axI, 596 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringer Texts in Statistics, _x1431-875X
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aStoffer, David S. _eautor _9301403
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781441978646
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4419-7865-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c285024 _d285024