Get Free Ebook Time Series Analysis by State Space Methods (Oxford Statistical Science Series)
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Time Series Analysis by State Space Methods (Oxford Statistical Science Series)
Get Free Ebook Time Series Analysis by State Space Methods (Oxford Statistical Science Series)
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About the Author
James Durbin is at London School of Economics and Political Science. Siem Jan Koopman is at Department of Econometrics, Free University, Amsterdam, The Netherlands.
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Product details
Series: Oxford Statistical Science Series (Book 24)
Hardcover: 253 pages
Publisher: Oxford Univ Pr; 1 edition (August 1, 2001)
Language: English
ISBN-10: 0198523548
ISBN-13: 978-0198523543
Product Dimensions:
6.4 x 0.8 x 9.5 inches
Shipping Weight: 1.1 pounds (View shipping rates and policies)
Average Customer Review:
4.4 out of 5 stars
12 customer reviews
Amazon Best Sellers Rank:
#536,767 in Books (See Top 100 in Books)
I think it's one of best books in state space model. It comes in 2012 and covers a lot of updates in the field. A few problems,1. not too many examples, so if you are new to SSM, it may be difficult for you to understand, and you need other books accompany, likeDLM with R, etc.2. This books' perspective is a bit from other books. In Dynamic Linear Model with R and the other text by Mike West, the treatment are similar3. Detailed algorithm is not included. And the code is not free to use.But overall, this is a very good book who anyone who want to be serious with State Space Models.
This is the last book by "Mr. Time Series." Durbin knew everyone involved in the development of modern statistical analysis of time series. This effort, written with Koopmans (of Commandeur and Koopmans) is a graduate-level presentation of state space methods, whereas the Commandeur/Koopmans effort can be shared with good undergraduates.
If you're on the hunt for a comprehensive and detailed mathematical treatment of State Space modeling, this book may be what you're looking for. It's a "heavy" textbook, not a "how-to" cookbook, but is well-organized and well-written. The first author was James Durbin, the renowned statistician who passed away in 2012 at the age of 88. His frequent collaborator, Siem Jan Koopman, is widely published on time series analysis and econometrics topics.
State space models are a general and broad time series method and overcome the difficulty of dealing with the stationarity of the Box-Jenkins approach. All ARIMA models can also be stated and handled in state space models. In general, it is called Kalman filter in engineering and statistics.Both authors are renowned researchers in time series analysis, especially in state space modeling. The book itself is mainly based on their publications and their colleagues' and is written from a statistical point of view. So many filters used in engineering such as extended Kalman filter (EKF) and sequential Monte Carlo (particle filter) were not included in it. There are two parts: Part I and Part II. Part I deals with linear Gaussian state space models including non-stationary time series analysis and one short chapter of Bayesian analysis. It's readable, but you should expect somewhat messy notations in some chapters. Part II deals with non-Gaussian and nonlinear state space models. Part II is solely based on both authors' seminal paper in 2000. Their paper in 2000 was cut significantly by the editor, so they took an opportunity to illustrate what was cut in detail in Part II. Bayesian analysis for non-Gaussian and nonlinear state space models is also included. Readers may have a little more difficulty reading Part II.There are two main cons of the book. First of all, the coverage of non-Gaussian and nonlinear state space models is very limited because the treatment they introduced is just their paper in 2000. So readers cannot be exposed to other popular methods in engineering such as EKF and particle filter. Second, their computing tools are Koopman's software, which is commercial. So readers will find it hard to apply state space models for examples in the book.However, in general, the book introduces the concept of Kalman filter nicely and rigorously.
Both authors of the book have authoritative stature in state space models. But this textbook is somehow stuck in a zombie land where it's neither fundamental enough to be an easy read like An Introduction to State Space Time Series Analysis (Practical Econometrics), nor in-depth enough to thoroughly cover more advanced topics such as non-Gaussian nonlinear state space models. Readers are simply directed to try Koopman's ssfpack (extended) or STAMP software, neither of which free.
Good book to learn how to do filtering. clear and concise. I havent finished the book but I believe they could add more on high dimensional problems.
good.
Part I - The linear Gaussian state space model is a must for the understanding the applications, with plenty of examples. Easy to read and understand, it will certainly help the practicioner in applying its concepts with any statistical software, or even in writing his/her own code. Part II - Non-gaussian and non-linear state space models, on the other hand, jumps into a mucho more exoteric field, and requires from the reader a much deeper knowledge on the subjects covered, requiring further consultation to the rich bibliography mentioned in it.
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