- Series: Use R!
- Paperback: 252 pages
- Publisher: Springer; 2009 edition (June 2, 2009)
- Language: English
- ISBN-10: 0387772375
- ISBN-13: 978-0387772370
- Product Dimensions: 6.2 x 0.6 x 9 inches
- Shipping Weight: 15.5 ounces (View shipping rates and policies)
- Average Customer Review: 7 customer reviews
- Amazon Best Sellers Rank: #668,499 in Books (See Top 100 in Books)
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Dynamic Linear Models with R (Use R!) 2009th Edition
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the Use R! series for providing a valuable collection of books for a fantastic open-source software.” (American Statistician, August 2010, Vol. 64, No. 3)
From the Back Cover
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms.
The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online.
No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Giovanni Petris is Associate Professor at the University of Arkansas. He has published many articles on time series analysis, Bayesian methods, and Monte Carlo techniques, and has served on National Science Foundation review panels. He regularly teaches courses on time series analysis at various universities in the US and in Italy. An active participant on the R mailing lists, he has developed and maintains a couple of contributed packages.
Sonia Petrone is Associate Professor of Statistics at Bocconi University,Milano. She has published research papers in top journals in the areas of Bayesian inference, Bayesian nonparametrics, and latent variables models. She is interested in Bayesian nonparametric methods for dynamic systems and state space models and is an active member of the International Society of Bayesian Analysis.
Patrizia Campagnoli received her PhD in Mathematical Statistics from the University of Pavia in 2002. She was Assistant Professor at the University of Milano-Bicocca and currently works for a financial software company.
Top customer reviews
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I believe that the quality of this book must be appreciated in context. As one reviewer stated (and I fully support that) - this is a very good description of how to apply the dlm package with well-selected examples. Its strengths are: (1) very well written package - I did learn some new things about R while analyzing the code; (2) good examples illustrating finer points of dynamic modeling in multiple contexts; (3) clear though sometimes terse explanations of the overall field.
Since this is the best book on application I have found (through the years) it definitely deserves 5 stars. That does not mean it is perfect for everybody.
I disagree with the 3-star review [with the exception of 'The software package is itself very powerful end elegantly implemented' :-)].
This book is not be-all-end-all and it does not attempt to be. The theoretical basis and numerous - really numerous - and well explained practical examples are contained in 680 pages of 'Bayesian Forecasting and Dynamic Models' by West and Harrison. More recent 'Time Series: Modeling, Computation, and Inference ' by Prado and West contains plenty of explanations using similar methods and gives a good update on theory. There are many other books, though I found 'Time Series Analysis by State Space Methods' by Durbin and Koopman (2001 version) rather dry and tough going for somebody without earlier experience in this area.
I think that the learning curve is slightly sharper in state space than in more traditional ARIMA-based approach. Also, the more traditional approach has simply many more books published on various level, most of that introductory. Still, if you want to use better version of modeling that is a small price you have to pay.
Possibly some will expect this book to be more like (excellent in that area) 'An Introduction to Analysis of Financial Data with R' by Ruey Tsay which can be 'consumed' without much external reading and there is plenty of R examples to illustrate most of the element of the traditional approach. However, note that Tsay's book has an _introduction_ in the title, thus different audience. Additional advantage is the limitation of the topic to the financial time series - while 'Dynamic Linear Model with R' are for multitude of application areas.
Personally I wish the authors found time to create a second edition of this book with some updates to the methods etc. - though I do appreciate that the market for such books is small. For general state space and dynamic modeling field a book similar in approach to Tsay's 'An Introduction to Analysis of Financial Data with R' could result in wider use of that approach.
This is a good buy for any applied statistician learning or using state-space models.
Although it stands on its own, I think many readers would like to have at times recourse to lengthier books such as Time Series Analysis by State Space Methods (Oxford Statistical Science Series) or Bayesian Forecasting and Dynamic Models (Springer Series in Statistics). Readers unfamiliar with R should read first one of the many good introductions in existence, such as Introductory Statistics with R (Statistics and Computing),Modern Applied Statistics with S or Probability and Statistics with R, to name a few.
All in all, a welcomed addition to time series literature in R and a valuable complement to an outstanding R package.
Unfortunately the Kindle edition suffers from occasional but significant OCR issues in the math in the running text, and for a mathematical book that's a killer -- *all* the formatting needs to be preserved *all* the time, or the equations and their explanation don't make sense. Learn from my mistakes and save yourself the $60: buy the paper version *first*.