
Time Series Analysis and Its Applications
Springer (Publisher)
Published on 1. March 2005
Book
Hardback
XIII, 551 pages
978-0-387-98950-1 (ISBN)
Article exhausted; check for reprint
Description
A balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems, such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. Although designed as a text for graduate level students in statistics and the physical, biological and social sciences, some parts of the book will also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels, and the material has been updated by adding modern developments involving categorical time series analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. The book is supplemented by data and an exploratory time series analysis program ASTSA for Windows that can be downloaded from the Web as freeware.
More details
Series
Edition
1st ed. 2000. Corr. 5th printing
Language
English
Place of publication
NY
United States
Target group
College/higher education
Professional and scholarly
Illustrations
97
97 s/w Abbildungen
152 figs.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Thickness: 33 mm
Weight
975 gr
ISBN-13
978-0-387-98950-1 (9780387989501)
DOI
10.1007/978-1-4757-3261-0
Schweitzer Classification
Other editions
New editions

Book
05/2006
2nd Edition
Springer
€91.97
Article exhausted; check for reprint
Additional editions

Robert H. Shumway | David S. Stoffer
Time Series Analysis and Its Applications
E-Book
03/2013
1st Edition
Springer
€85.59
Available for download
Content
1: Characteristics of Time Series.- 2: Time Series Regression and ARIMA Models.- 3: Spectral Analysis and Filtering.- 4: State-Space and Multivariate ARMAX Models.- 5: Statistical Methods in the Frequency Domain.- References.