Description
Book SynopsisRegression methods have been an integral part of time series analysis. Developments have made major strides in such areas as non continuous data where a linear model is not appropriate. This is a review of the regression methods in time series analysis.
Trade Review"...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)
Table of ContentsDedication.
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.
