Regression Models for Time Series Analysis by Benjamin Kedem
Regression 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.-
Introductory Statistics for Business and Economics
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Applied Longitudinal Analysis
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Biostatistics
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Sample Size Determination and Power
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Statistical Methods for Survival Data Analysis
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Applied Econometric Times Series
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Bayesian Analysis for the Social Sciences
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An Elementary Introduction to Statistical Learning Theory
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The Statistical Analysis of Failure Time Data
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Loss Models
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Statistical Rules of Thumb
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Bayesian Theory
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Graphical Models in Applied Multivariate Statistics
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Introduction to Linear Regression Analysis
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Aspects of Multivariate Statistical Theory
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Fundamentals of Queueing Theory
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Statistics of Extremes
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Structural Equations with Latent Variables
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Nonparametric Statistical Methods
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Applied Linear Regression
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Models for Investors in Real World Markets
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Regression Analysis by Example
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Directional Statistics
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Statistical Intervals
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An Introduction to Probability Theory and Its Applications, Volume 2
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System Reliability Theory
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An Introduction to Categorical Data Analysis
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Markov Decision Processes
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Applied Logistic Regression
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Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
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Probability and Measure
"..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)
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.
| SKU | Unavailable |
| ISBN 13 | 9780471363552 |
| ISBN 10 | 0471363553 |
| Title | Regression Models for Time Series Analysis |
| Author | Benjamin Kedem |
| Series | Wiley Series In Probability And Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Wiley-Interscience |
| Year published | 2002-09-02 |
| Number of pages | 360 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |