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Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)by Robert H. Shumway
Synopses & ReviewsPublisher Comments:<div style="MARGIN: 0in 0in 10pt"><em>Time Series Analysis and Its Applications</em> 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 statespace 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 Monte Carlo Markov chain integration methods. The third edition includes a new section on testing for unit roots and the material on statespace modeling, ARMAX models, and regression with autocorrelated errors have been expanded. </div>
<div style="MARGIN: 0in 0in 10pt">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 copyandpaste 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. </div> Synopsis:Time Series Analysis and Its Applications, presents a comprehensive treatment of both time and frequency domain methods with accompanying theory. Extensive examples illustrate solutions to climate change,
Synopsis:<div style="MARGIN: 0in 0in 10pt"><em>Time Series Analysis and Its Applications</em> 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 statespace 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 Monte Carlo Markov chain integration methods. The third edition includes a new section on testing for unit roots and the material on statespace modeling, ARMAX models, and regression with autocorrelated errors have been expanded. </div><div style="MARGIN: 0in 0in 10pt">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 copyandpaste 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. </div>
Synopsis:Time 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,
About the Author<div style="MARGIN: 0in 0in 10pt">Robert H. Shumway is Professor Emeritus 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 and served as a Departmental Editor for the <em>Journal of Forecasting</em> and Associate Editor for the <em>Journal of the American Statistical Association</em>. </div> <span style="FONTSIZE: 11pt; LINEHEIGHT: 115%">David S. Stoffer is Professor of Statistics at the University of Pittsburgh. He is a Fellow of the American Statistical Association. 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 of the <em>Journal of Forecasting</em> and an Associate Editor of the <em>Annals of Statistical Mathematics</em>. He has served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the <em>Journal of the American Statistical Association</em></span>
Table of ContentsCharacteristics of time series. Time series regression and exploratory data analysis. ARIMA models. Spectral analysis and filtering. Additional time domain topics. Statespace models. Statistical methods in the frequency domain.
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