| Reviews |
| - '... provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis.' - Journal of the Royal Statistical Society
- 'This book will be helpful to graduate students and applied statisticians working in the area of econometric modelling as well as researchers in the areas of engineering, medicine and biology where state space models are used.' - Journal of the Royal Statistical Society
- '... a good mixture of theory and practical applications ... graduate and research students will definitely enjoy this book. Also practitioners will find the book quite useful. I would also recommend it for library purchase.' - Journal of the Royal Statistical Society
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| Description | | - Clear and comprehensive expostion written by leaders in the field.
- Includes new material not available elsewhere
| | 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 and disturbence 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 book provides an excellent source for the development of practical courses on time series analysis. |
Readership: Researchers in statistics, econometrics, biometrics, environmetrics, engineering, system theory, and physics. Financial analysts in banking and other financial institutions.
| Contents |
1.
Introduction
I. THE LINEAR GAUSSIAN STATE SPACE MODEL
2.
Local Level Model
3.
Linear Gaussian state space models
4.
Filtering, smoothing and forecasting
5.
Initialisation of filter and smoother
6.
Computational aspects of filtering and smoothing
7.
Maximum likelihood estimation
8.
Bayesian analysis
9.
Illustrations of the use of the linear Gaussian Model
II. NON-GAUSSIAN AND NONLINEAR STATE SPACE MODELS
10.
Non-Gausian and nonlinear state space models
11.
Importance sampling
12.
Analysis from a classical standpoint
13.
Analysis from a Bayesian standpoint
14.
Non-Gaussian and nonlinear illustrations
References
Author Index
Subject Index
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| Authors, editors,
and contributors |
J. Durbin, Department of Statistics, London School of Economics and Political Science and
S. J. Koopman, Department of Econometrics, Free University, Amersterdam
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limitation price, format, extent, number of illustrations,
and month of publication, was as accurate as
possible at the time the catalogue was compiled.
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