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Скачать Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics): Robert H. Shumway, David S. Stoffer бесплатно


Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics): Robert H. Shumway, David S. Stoffer
Springer | ISBN: 0387293175 | 2006-05-25 | PDF (OCR) | 575 pages | 5.59 Mb

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 non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging or 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. Material from the earlier 1988 Prentice-Hall text Applied Statistical Time Series Analysis has been updated by adding modern developments involving categorical time sries analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. These add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models. The book is complemented by ofering accessibility, via the World Wide Web, to the data and an exploratory time series analysis program ASTSA for Windows that can be downloaded as Freeware. Robert 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 Inernational 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 and is currenlty a Departmental Editor for the Journal of Forecasting. David 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 an Associate Editor of the Journal of Forecasting and has served as an Associate Editor for the Journal fo the American Statistical Association.
Summary: Adequate but need a refresher
Rating: 3
The examples are interesting and informative, but it's been a few years since I took a statistics course and I had forgotten some of the basic manipulations necessary to work through the homeworks. It's still early in the course, but I think that the book and R examples will be more than adequate as an assist to lecture.
Summary: The best of a bad bunch
Rating: 4
Although a lot of books have been written on time series analysis, most of them just aren't very good. "Time Series Analysis and its Applications" is one of the better time series text books. It's not a brilliant book, but all of the other time series books that I have seen are worse.
This book covers all of the main areas of time series analysis such as ARIMA, GARCH and ARMAX models and spectral analysis and it does a pretty good job of it. Most of the explanations are clear enough for a beginner (with some statistical background) and are accompanied by worked examples (something which seems to be omitted in a lot of time series texts). Exercises are also provided at the end of each chapter, although no solutions are provided in the book (a colleague of mine informed me that the solutions are provided on the author's website, but that a large portion of these are either wrong or poorly explained).
Prospective purchasers of this book should be aware, however, that there are a number of errors throughout this book (corrections can be found on the author's website) and that although the title suggests that there are "R examples" in this book, these examples are few and far between and are not well explained. If you are looking for a manual for the R time series functions, then this is not the book for you.
I am a university lecturer and set this book as a supplementary text for an undergraduate statistics unit I teach, which includes a time series component. I believe that this is the best book available for this purpose. However, if you are a lecturer who is thinking of setting this book as a text for your class, please be aware of its limitations, and make sure that your students are also aware of them.
Summary: Excellent!
Rating: 5
I got this book very fast and it is also an excellent textbook to learn time series.
Summary: modern time series with applications
Rating: 4
This is a modern book on time series analysis with many interesting and useful examples. It has a practical orientation much like Shumway's earlier book. The material has been tested in courses given by the authors at UC Berkeley and UC Davis. Good for both undergraduate and graduate level students. It covers most of the basics from both the time and frequency domain approaches. Although one reviewer suggests that it is light on theory compared to the Brockwell and Davis book, there is an adequate amount of theory presented which makes the level intermediate. It does require some advanced mathematics. Interesting topics not commonly found in competitor books include long memory ARMA models, the multivariate ARMAX models and their state space representation, applications of ARMAX models to longitudinal data analysis, bootstrapping state space models and the use of frequency domain time series methods applied to discriminant analysis, clustering and various other common multivariate statistical techniques. It also has a nice list of references. It definitely deserves 5 stars and possibly an oscar!
Summary: One of the book for your research
Rating: 5
I required to use ARIMA in my research. This book give me a great guideline. It will simplify a very complex material with practical examples.

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