Regression Models for Time Series Analysis
Language: English
Published by Wiley-Interscience, 2002
- Hardcover
- Used
Seller: Anybook.com, Lincoln, United KingdomAnybook.com
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Condition: Used - Good
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Add to basketItem description from seller
This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780471363552.
Seller Inventory # 2933923
- Title
- Regression Models for Time Series Analysis
- Author
- Kedem, Benjamin
- Publisher
- Wiley-Interscience
- Publication year
- 2002
- Condition
- Good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0471363553
- ISBN 13
- 9780471363552
- Item weight
- 750 grams
- Seller catalogs
- Mathematics
Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis.
Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data.
The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements.
Notably, the book covers:
* Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling
* Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm
* Prediction and interpolation
* Stationary processes
"Synopsis" may belong to another edition of this title.
About the Author
KONSTANTINOS FOKIANOS, PhD, is Assistant Professor in the Department of Mathematics and Statistics at the University of Cyprus.
"About the title" may belong to another edition of this title.
Anybook.com
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