Dynamic Linear Models with R

by Giovanni Petris, Sonia Petrone, Patrizia Campagnoli

Overview

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.

Product Details

ISBN-13:	9780387772370
Publisher:	Springer New York
Publication date:	06/02/2009
Series:	Use R!
Edition description:	2009
Pages:	252
Product dimensions:	6.10(w) x 9.10(h) x 0.60(d)

Table of Contents

Introduction: basic notions about Bayesian inference.- Dynamic linear models.- Model specification.- Models with unknown parameters.- Sequential Monte Carlo methods.

Editorial Reviews

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 Publisher