Regression Models for Time Series Analysis

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John Wiley & Sons, Mar 11, 2005 - Mathematics - 360 pages
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A thorough review of the most current regression methods in time series analysis
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
 

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Contents

1 Time Series Following Generalized Linear Models
1
2 Regression Models for Binary Time Series
49
3 Regression Models for Categorical Time Series
89
4 Regression Models for Count Time Series
139
5 Other Models and Alternative Approaches
175
6 State Space Models
213
7 Prediction and Interpolation
249
Appendix Elements of Stationary Processes
285
References
297
Index
327
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About the author (2005)

BENJAMIN KEDEM, PhD, is Professor of Mathematics at the University of Maryland.

KONSTANTINOS FOKIANOS, PhD, is Assistant Professor in the Department of Mathematics and Statistics at the University of Cyprus.

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