000 04346nam a22003975i 4500
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008 150903s2006 xxu| o |||| 0|eng d
020 _a9780387362762
_99780387362762
024 7 _a10.1007/0387362762
_2doi
035 _avtls000331281
039 9 _a201509030720
_bVLOAD
_c201404120621
_dVLOAD
_c201404090401
_dVLOAD
_c201401311409
_dstaff
_y201401301206
_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.
250 _aSecond Edition.
264 1 _aNew York, NY :
_bSpringer New York,
_c2006.
300 _axiii, 575 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, Second Edition, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging, monitoring a nuclear test ban treaty, evaluating the volatility of an asset, or finding a gene in a DNA sequence. 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. Material from the first edition of the text has been updated by adding examples and associated code based on the freeware R statistical package. As in the first edition, modern developments involving categorical time series analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, GARCH models, stochastic volatility models, wavelets, and Monte Carlo Markov chain integration methods are incorporated in the text. In this edition, the material has been divided into smaller chapters, and the coverage of financial time series, including GARCH and stochastic volatility models, has been expanded. These topics add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models. R.H. Shumway is Professor of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He won the 1986 American Statistical Association Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center Statistics Award; both awards were for joint papers on time series applications. He is the author of a previous 1988 Prentice-Hall text on applied time series analysis. D.S. Stoffer is Professor of Statistics at the University of Pittsburgh. He has made seminal contributions to the analysis of categorical time series and won the 1989 American Statistical Association Award for Outstanding Statistical Application in a joint paper analyzing categorical time series arising in infant sleep-state cycling. He is currently a Departmental Editor for the Journal of Forecasting and Associate Editor of the Annals of the Institute of Statistical Mathematics.
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:
_z9780387293172
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/0-387-36276-2
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c278078
_d278078