Regression Models for Time Series Analysis

Beschreibung

A thorough review of the most current regression methods in timeseries analysis Regression methods have been an integral part of time seriesanalysis for over a century. Recently, new developments have mademajor strides in such areas as non-continuous data where a linearmodel is not appropriate. This book introduces the reader to newerdevelopments and more diverse regression models and methods fortime series analysis. Accessible to anyone who is familiar with the basic modern conceptsof statistical inference, Regression Models for Time SeriesAnalysis provides a much-needed examination of recent statisticaldevelopments. Primary among them is the important class of modelsknown as generalized linear models (GLM) which provides, under someconditions, a unified regression theory suitable for continuous,categorical, and count data. The authors extend GLM methodology systematically to time serieswhere the primary and covariate data are both random andstochastically dependent. They introduce readers to variousregression models developed during the last thirty years or so andsummarize classical and more recent results concerning state spacemodels. To conclude, they present a Bayesian approach to predictionand interpolation in spatial data adapted to time series that maybe short and/or observed irregularly. Real data applications andfurther results are presented throughout by means of chapterproblems 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, MonteCarlo, and the EM-algorithm * Prediction and interpolation * Stationary processes

Autor*in

Benjamin Kedem

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"...provides an excellent overview of modern regression methods intime series analysis...accessible and illustrative...a valuableresource to students, researchers, and practitioners. Thetext reflects a deep appreciation of both theory and applications,as well as a comprehensive understanding of a set of modelingframeworks that are increasingly integral to modern time seriesanalysis." (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 beuseful for both practitioners and researchers..." (Short BookReviews, April 2003) "...can be recommended to teachers and students as material forseminars and special lectures...very useful for appliedstatisticians." (Zentralblatt Math, Vol.1011, No.11,2003) "...introduces the reader to relatively newer and somewhat morediverse regression models and methods for time series analysis thanmost standard texts." (Quarterly of Applied Mathematics,Vol. LXI, No. 2, June 2003) "...I gladly recommend this book..." (Technometrics, Vol.45, No. 4, November 2003)
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