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by James Durbin,Siem Jan Koopman
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Mathematics
  • Author:
    James Durbin,Siem Jan Koopman
  • ISBN:
    0198523548
  • ISBN13:
    978-0198523543
  • Genre:
  • Publisher:
    Oxford Univ Pr; 1 edition (August 1, 2001)
  • Pages:
    253 pages
  • Subcategory:
    Mathematics
  • Language:
  • FB2 format
    1986 kb
  • ePUB format
    1465 kb
  • DJVU format
    1395 kb
  • Rating:
    4.7
  • Votes:
    375
  • Formats:
    lrf lit azw doc


Publication Date: May 3, 2012.

Time Series Analysis by State Space Methods (Oxford Statistical Science Series). James Durbin, Siem Jan Koopman. Download (djvu, . 8 Mb) Donate Read.

The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. Oxford Statistical Science Series. Clear, comprehensive introudction to the state space approach to time series analysis. Written by leaders in the field. Complete treatment of linear Gaussian models.

We study state-of-the-art methods for time series analysis and assess the benefits and drawbacks of each one of them.

Vrije Universiteit Amsterdam. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. We study state-of-the-art methods for time series analysis and assess the benefits and drawbacks of each one of them.

James Durbin (author), Siem Jan Koopman (author) . Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations. Part II extends the treatment to nonlinear and non-normal models.

This excellent text provides a comprehensive treatment of the state space approach to time series analysis. Publisher:Oxford University Press, Incorporated.

book by James Durbin. This excellent text provides a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements anddisturbence terms, each of which is modelled separately.

Start by marking Time Series Analysis by State Space Methods (Oxford Statistical Science Series Book 38) as. .

Start by marking Time Series Analysis by State Space Methods (Oxford Statistical Science Series Book 38) as Want to Read: Want to Read savin. ant to Read.

This excellent text provides a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements anddisturbence terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. The bookprovides an excellent source for the development of practical courses on time series analysis.

Benn
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.
Yramede
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.
Dianalmeena
good.
Kanal
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.
Arryar
Chapter 2 of this book must rank among the very best texts
ever written on the Kalman Filter: In a few pages, the authors
not only give a quick, comprehensable, implementable demo of
the Kalman filter (I had an implementation of the equations
up an working less than half an hour after I first opened the
book); they also motivate the various topics to be treated
in the rest of the book, like initialization, smoothing,
error control and so on.

Then... they fall through. While a lot of the simpler theory
is explained if not easily so at least comprehensable, the
authors tend to fall back on the 'we refer to the computational
package for further details' tretament way too often. Quite
frustrating to work through five pages of intense linear
algebra only to find that the crux of the chapter isn't
in there at all.

If there ever is a 2nd edition of this text, PLEASE make it
completely self-contained!

As for rating, the book as a whole might deserve 3 to 4 stars.
But that chapter 2... that chapter is worth 5 stars alone.

Easily.