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Focusing on Bayesian approaches and computations using 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.
- Sales Rank: #1992285 in eBooks
- Published on: 2010-05-21
- Released on: 2010-05-21
- Format: Kindle eBook
Review
The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.
―Hsun-Hsien Chang, Computing Reviews, March 2012
My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.
―William Seaver, Technometrics, August 2011
… a very modern entry to the field of time-series modelling, with a rich reference list of the current literature, including 85 references from 2008 and later. It is well-written and I spotted very few typos. This textbook can undoubtedly work as a reference manual for anyone entering the field or looking for an update. … I am certain there is more than enough material within Time Series to fill an intense one-semester course.
―International Statistical Review (2011), 79
About the Author
Raquel Prado is an associate professor in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz.
Mike West is the Arts & Sciences Professor of Statistical Science in the Department of Statistical Science at Duke University.
Most helpful customer reviews
12 of 12 people found the following review helpful.
An important book
By Dimitri Shvorob
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.
3 of 3 people found the following review helpful.
Nice applied text on time series analysis
By gdinolov
Mostly Bayesian in nature, this book is an excellent introduction to time series analysis, with important results shown and a plethora of examples of application.
4 of 6 people found the following review helpful.
One of the author's former student taught Introductory Time Series ...
By Arondes
One of the author's former students taught Introductory Time Series course in our university using this book. A student in that class came to me for help because she can not understand it. As a PhD in Statistics, I find that this one is way too hard for entry level. First off, if you do not know Bayesian/MCMC and all the "fashion" staff, you have no chance to understand it. Secondly, this book is moving too fast in "traditional" topics so the student will not have a solid background of "common sense". Unless you already know what the author is going to talk, you are unlikely to understand what he talks. You may take it as a reference book when you are doing some serious research, but do not expect it as introductory textbook.
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