Average Reviews:
(More customer reviews)Finally, a solid and accessible (if a little dry - more, and varied, examples would help motivate the various improvements) monograph that can be used in a Ph.D. course on Bayesian time series modeling; I wish I had it, in addition to Hamilton's tome, when I was in grad school. I look forward to "Time series" gaining an audience among current and future researchers, and helping integrate Bayesian methods into statistics and econometrics curricula.
The book can be compared to "Bayesian forecasting and dynamic models" by West and Harrison - the "Old Testament" on DLMs, more thorough/technical/specialized, less up-to-date - and "Dynamic linear models with R" by Petris, Petrone and Campagnoli, which takes a "softer", introductory approach, but gets to some of the more challenging topics, explored in "Time series", in its final chapters.
I suggest using "Search inside" to read Preface on page xviii, then going back to the table of contents.
Click Here to see more reviews about: Time Series: Modeling, Computation, and Inference (Chapman & Hall/CRC Texts in Statistical Science)
Focusing on Bayesian approaches and computations using up-to-date simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers.The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. They illustrate the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, and finance. Data sets, R and MATLAB code, and other material are available on the authors' websites.Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.
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