Regression Models for Time Series Analysis
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More About This Title Regression Models for Time Series Analysis
English
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
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
English
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.
KONSTANTINOS FOKIANOS, PhD, is Assistant Professor in the Department of Mathematics and Statistics at the University of Cyprus.
English
Dedication.
Preface.
Times Series Following Generalized Linear Models.
Regression Models for Binary Time Series.
Regression Models for Categorical Time Series.
Regression Models for Count Time Series.
Other Models and Alternative Approaches.
State Space Models.
Prediction and Interpolation.
Appendix: Elements of Stationary Processes.
References.
Index.
Preface.
Times Series Following Generalized Linear Models.
Regression Models for Binary Time Series.
Regression Models for Categorical Time Series.
Regression Models for Count Time Series.
Other Models and Alternative Approaches.
State Space Models.
Prediction and Interpolation.
Appendix: Elements of Stationary Processes.
References.
Index.
English
"...provides an excellent overview of modern regression methods in time series analysis...accessible and illustrative...a valuable resource to students, researchers, and practitioners. The text reflects a deep appreciation of both theory and applications, as well as a comprehensive understanding of a set of modeling frameworks that are increasingly integral to modern time series analysis." (Journal of the American Statistical Association, March 2004)
"...highly recommended..." (Choice, Vol. 40, No. 6, February 2003)
"...the book does what it sets out to do very well and will be useful for both practitioners and researchers..." (Short Book Reviews, April 2003)
"...can be recommended to teachers and students as material for seminars and special lectures...very useful for applied statisticians." (Zentralblatt Math, Vol.1011, No.11, 2003)
"...introduces the reader to relatively newer and somewhat more diverse regression models and methods for time series analysis than most standard texts." (Quarterly of Applied Mathematics, Vol. LXI, No. 2, June 2003)
"...I gladly recommend this book..." (Technometrics, Vol. 45, No. 4, November 2003)
