Time series: Modeling, Computation, and Inference, by Raquel Prado and Mike West, Boca Raton, Chapman & Hall/CRC, 2010, xx + 353 pp., £59.99 or US$94.95 (hardback), ISBN 9781420093360
The book, with its 10 chapters, represents a good introduction to Bayesian analysis of time series; the exposition includes theory, methods and applications.
The book analyses time series in both time domain and frequency domain, and proposes various models for them. Also, it investigates properties of these models considering certain continuous marginals for modelling. Moreover, it concentrates on Bayesian analysis of the proposed models using different techniques, like Markov chain Monte Carlo and sequential Monte Carlo.
The material is valuable and has a good representation of the subject. It represents a good course to graduate level and also provides good material to all who are interested in Bayesian time series analysis. Despite this, the book is not perfect. It proposes many Bayesian ideas without going deeply into Bayesian estimation and inference for model parameters. Also, it does not cover recent models such as integer-valued time series or categorical time series (see, for example 1).
Reference
- Kedem , B. and Fokianos , K. 2002 . Regression Models for Time Series Analysis , Chichester : Wiley .