Time Series Analysis and Its Applications : With R Examples
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The work Time Series Analysis and Its Applications : With R Examples represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
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Time Series Analysis and Its Applications : With R Examples
Resource Information
The work Time Series Analysis and Its Applications : With R Examples represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
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 Time Series Analysis and Its Applications : With R Examples
 Title remainder
 With R Examples
 Statement of responsibility
 by Robert H. Shumway, David S. Stoffer
 Language

 eng
 eng
 Summary
 Time 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 nontrivial 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 statespace 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 PrenticeHall 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 sleepstate cycling. He is currently a Departmental Editor for the Journal of Forecasting and Associate Editor of the Annals of the Institute of Statistical Mathematics.
 Dewey number
 519.5
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut

 j6fd4MMA1fM
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 Language note
 English
 LC call number
 QA276280
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Springer Texts in Statistics,
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