Regression Models for Time Series Analysis (eBook)

Benjamin Kedem, Konstantinos Fokianos (Autoren)

eBook Download: PDF

2005 | 1. Auflage
360 Seiten
John Wiley & Sons (Verlag)
978-0-471-46168-5 (ISBN)

Infos

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

Autor

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.

Inhalt

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.

Rezensionen

"...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)

Produkt-Details

• Erscheint lt. Verlag: 11.3.2005
• Reihe/Serie: Wiley Series in Probability and Statistics
• Reihe/Serie: Wiley Series in Probability and Statistics
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Schlagworte: Regression Analysis • Regressionsanalyse • Statistics • Statistik • Time Series • Zeitreihen • Zeitreihenanalyse
• ISBN-10: 0-471-46168-7 / 0471461687
• ISBN-13: 978-0-471-46168-5 / 9780471461685