Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) | 
 enlarge | Authors: Robert H. Shumway, David S. Stoffer
Publisher: Springer
Category: Book
List Price: $99.00
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Rating:  23 reviews
Sales Rank: 188682
Media: Hardcover
Edition: 2nd
Pages: 575
Number Of Items: 1
Shipping Weight (lbs): 2.2
Dimensions (in): 9.3 x 6.3 x 1.3
ISBN: 0387293175
Dewey Decimal Number: 519
EAN: 9780387293172
Publication Date: May 25, 2006
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Product Description
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.
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| Customer Reviews: Read 18 more reviews...
 A review from someone who has actually read the book February 19, 2007
Nicholas White (Massachusetts)
10 out of 11 found this review helpful
As the title implies, the book is a text on time series analysis and its applications. It is a modern treatment of time series analysis with a slant toward applications. The applications are interesting and involve current topics such as global warming. The examples are broad in range, including data from various fields such as biology, economics, engineering, environmental science, and medicine. The book is interesting and accessible, and it provides an excellent introduction various aspects of the analysis of time series. The text covers both the spectral and time domains, including a thorough presentation of state-space models. The basic requirement for being able to understand most of the text is knowing the material that would be covered in introductory courses on regression and mathematical statistics.
The book has many interesting and "real" (as opposed to "toy") examples and, as the subtitle explains, many of the examples have associated R code. This makes for a positive experience because you can replicate the analyses. Accordingly, there is no guessing as to what was done to obtain the results of an example. It is completely wrong to say that "R is not relevant". But you do not have to take my word for it! Just go to the website for the text at StatLib and all the R code in the text is posted there. In addition, as the authors' state in the Preface (which is also available for viewing at the website for the text), R code for the state-space chapter (Chapter 6) is on the website for the text. There you will find code for the Kalman filter and smoothing algorithms, as well as the EM algorithm and some examples for maximum likelihood estimation. The website for the text also has a small tutorial for a quick start on using R to do time series analysis. The tutorial is great for a beginner.
At the end of the day, the text is not an R manual. It never says it is and I do not understand why anyone would think it should be an R manual. It is also not a manual for making wine and it will not help you train your dog. It is, however, an accessible modern introduction to time series analysis with many interesting examples that have associated R code. And, while you are learning time series analysis, you will also learn how to use R for analyzing time series.
An Oscar Winning Book on Time Series Analysis May 11, 2000
RJF HUDSON (NOOSA HEADS, QUEENSLAND AUSTRALIA)
11 out of 13 found this review helpful
Dr Shumway and Dr Stoffer have produced a book upon time series analysis that will become an industry and academic standard. All those mathematical and diagnostic frighteners that have been sidestepped by many other authors have been introduced by the authors and used in such a simplifying way that students of all sciences, not only economics, will richly enjoy reading and putting into use. Garch,Bootstrapping, State-Space, Long-memory, if it is modern then it is covered in detail with plenty of top-notch examples. I give the book 5 stars and an Oscar.
well written text, mixing theory and applications August 7, 2000
Michael R. Chernick (Malvern, PA)
13 out of 17 found this review helpful
This is a modern book on time series analysis with many interesting and useful examples. It has a practical orientation much like Shumay'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!
Excellent September 9, 2001
Andrew Godunov (Moscow)
5 out of 6 found this review helpful
This book is something... I've read it twice and still return to some chapter from time to time. It really requires patience and a strong mathematical background to get through some chapter but it gives you a knowledge and confidence in the modern time series statistics. The text is quite dense and concentrated but I like it.
A great book for students August 29, 2004
Nick P (San Rafael, CA)
6 out of 8 found this review helpful
I had a course from this text last year and I think this is a great book for students. We covered parts of Chapters 1-4 including ARMA models, spectral analysis and state-space models. It seems like most texts on time series explain a concept and then use a trite example to demonstrate the concept. With this text, the emphasis is on the applications. Concepts are presented as part of an analysis of a substantive data set. In addition to fundamental ideas, the authors discuss topics in modern time series analysis such as modern regression, long memory, GARCH, and MCMC. I found the material easy to read and I thought the problems were at an appropriate level.
I found most texts on time series to be either theory oriented or watered down and simple. Many texts concentrate on only the time domain or only the spectral domain. This text is somewhere in the middle, giving enough theory about a wide scope of topics to understand concepts at a deep enough level to apply the material with confidence.
I wouldn't usually post a review, but I liked this book so much that I felt a duty to rebut some of the nasty things said about the text by other students. For example, the time domain is basically difference equations. One reviewer said that difference equations are spread out throughout the text. Well, since three chapters are on time domain topics I would guess that difference equation ideas would be spread out in the three chapters. Also, the trend in time series texts, maybe starting with Box and Jenkins, is to use lower case letters for random variables. And who cares if you use a lower case letter an upper case letter or a picture of a dog to represent a random variable? If the notation is consistent, that is all that is needed. I do agree that you have to fill in some of the details in problems yourself. But isn't that what education is all about? You don't want everything spoonfed to you- you won't learn anything that way!
Finally, this is a wonderful text that covers a wide range of modern topics at an accessible level for most students with a basic knowledge of mathematical statistics. I agree with the reviewer who said this book deserves an oscar!
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