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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.
- Sales Rank: #1012635 in Books
- Brand: Brand: Springer
- Published on: 2010-04-02
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .61" w x 6.25" l, .83 pounds
- Binding: Paperback
- 252 pages
- Used Book in Good Condition
Review
“This book is a welcome addition to the series Use R! The text is interspersed with snippets of R code to illustrate the techniques and models and provides the basis of an excellent text for private study.” (International Statistical Review, 2010, 78, 1, 134-159)
“Dynamic Linear models With R provides a friendly introduction to the world of dynamic linear models (DLMs)… . This book provides the reader with the minimal tools necessary for Bayesian analysis of time series data using DLMs. …The main contribution…is the DLM package in R which provides functions for dynamic linear model creation as well as filtering, smoothing, and forecasting. Therefore, the book can be utilized as a descriptive manual that provides a hybrid practical-theoretical perspective on the purpose of the functions in this extremely useful R package. …I’d like to thank these authors for a useful applied Bayesian time series handbook suitable to a graduate statistics course and also to thank the editors of 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.
Most helpful customer reviews
10 of 10 people found the following review helpful.
Clear and to the point
By Cliente de Amazon
This book might be seen as extended documentation for the R package dlm, authored by Petris. It is in fact more than that. It discusses clearly the theory foundations from a Bayesian point of view, even if the length of the book implies that the discussion has to be terse at times. R code and examples are interspersed with the theory, greatly enhancing reader understanding.
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.
9 of 9 people found the following review helpful.
Work in progress...
By AGJr
The book is supposed to complement the poor community material available about the R package dlm, but fills its purpose only up to a certain extent. The reader will certainly need other support material to understand the concepts applied in this rich package (there are good suggestions in other reviews -- I found Durbin and Koopman - Time Series Analysis by State Space Models very helpful, but am waiting for Havey - Forecasting, Structural Time Series Models and the Kalman Filter right in the mail to complement it.) The book has several examples that help a lot on understanding how it works -- for the most basic models it works pretty well. But the implementation of more complex and no so complex models is somewhat cryptic, requiring a lot of trial-and-error and research from the reader. The software package is itself very powerful end elegantly implemented, and allows for very quick prototyping once you master it -- but do expect a very steep learning curve. And, even though the implemented algorithms are recursive by nature, there is no mention in the book on how to set up a "production model" that gets updated as new information arrives. In this sense, it is still "work in progress".
5 of 5 people found the following review helpful.
Excellent
By Dimitri Shvorob
I would have given the book five stars even if I had no interest in the dlm package or R in general. "Dynamic linear models with R" provides an effective and wide-ranging introduction to Bayesian time-series modeling, letting one *understand* the computations - so that, if desired, one can implement them in a programming language of their choice.
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