Dynamic Linear Models with R
(Sprache: Englisch)
This text introduces general state space models in detail before focusing on dynamic linear models, emphasizing their Bayesian analysis. It illustrates all the fundamental steps needed to use dynamic linear models in practice, using R.
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Produktinformationen zu „Dynamic Linear Models with R “
This text introduces general state space models in detail before focusing on dynamic linear models, emphasizing their Bayesian analysis. It illustrates all the fundamental steps needed to use dynamic linear models in practice, using R.
Klappentext zu „Dynamic Linear Models with R “
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.
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.
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.
Inhaltsverzeichnis zu „Dynamic Linear Models with R “
Introduction: basic notions about Bayesian inference.- Dynamic linear models.- Model specification.- Models with unknown parameters.- Sequential Monte Carlo methods.
Bibliographische Angaben
- Autoren: Giovanni Petris , Sonia Petrone , Patrizia Campagnoli
- 2009, 252 Seiten, Maße: 15,4 x 23,3 cm, Kartoniert (TB), Englisch
- Verlag: Springer, New York
- ISBN-10: 0387772375
- ISBN-13: 9780387772370
- Erscheinungsdatum: 02.06.2009
Sprache:
Englisch
Rezension zu „Dynamic Linear Models with R “
"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)
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