Time Series Analysis by State Space Methods
Series: Oxford Statistical Science Series; 24;
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Product details:
- Publisher Clarendon Press
- Date of Publication 21 June 2001
- ISBN 9780198523543
- Binding Hardback
- No. of pages272 pages
- Size 242x160x19 mm
- Weight 539 g
- Language English
- Illustrations numerous line figures 0
Categories
Short description:
This book presents a comprehensive treatment of the state space approach to time series analysis.
MoreLong description:
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.
... provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis.
Table of Contents:
Part I - The linear Gaussian state space models; Preface to Part I
Introduction
Local level model
Linear Gaussian state space models
Filtering, smoothing and forecasting
Initialisation of filter and smoother
Further computational aspects
Maximum likelihood estimation
Bayesian analysis
Illustrations of the use of the linear Gaussian model
Part II - Non-Gaussian and nonlinear state space models; Preface to Part II
Non-Gaussian and nonlinear state space models
Importance sampling
Analysis from a classical standpoint
Analysis from a Bayesian standpoint
Non-Gaussian and nonlinear illustrations
References
Author Index
Subject Index
