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Time Series
Analysis and Its Applications: With R Examples (Springer
Texts in Statistics) |
PRODUCT
DETAILS: Author: Robert
Shumway, David Stoffer Language: English Publisher: Springer Publication Date: 25 Nov 2010 Dimensions: 3.3 x 23 x 15.4 cm Format: Hardcover Pages: 604 Condition: NEW Product_ID: 144197D64X
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Time Series Analysis and Its
Applications presents a balanced and
comprehensive treatment of both time and frequency
domain methods with accompanying theory. Numerous
examples using nontrivial data illustrate solutions to
problems such as discovering natural and anthropogenic
climate change, evaluating pain perception experiments
using functional magnetic resonance imaging, and
monitoring a nuclear test ban treaty. The book is
designed to be useful as a text for graduate level
students in the physical, biological and social sciences
and as a graduate level text in statistics. Some parts
may also serve as an undergraduate introductory course.
Theory and methodology are separated to allow
presentations on different levels. In addition to
coverage of classical methods of time series
regression, ARIMA models, spectral analysis and
state-space models, the text includes modern
developments including categorical time series analysis,
multivariate spectral methods, long memory series,
nonlinear models, resampling techniques, GARCH models,
stochastic volatility, wavelets and Monte Carlo Markov
chain integration methods. The third edition
includes a new section on testing for unit roots and the
material on state-space modeling, ARMAX models, and
regression with autocorrelated errors have been
expanded.
Also new to this edition is the enhanced use of
the freeware statistical package R. In
particular, R code is now included in the text for
nearly all of the numerical examples. Data sets
and additional R scripts are now provided in one file
that may be downloaded via the World Wide
Web. This R supplement is a small compressed
file that can be loaded easily into R making all the
data sets and scripts available to the user with one
simple command. The website for the text
includes the code used in each example so that the
reader may simply copy-and-paste code directly into
R. Appendix R, which is new to this edition,
provides a reference for the data sets and our R scripts
that are used throughout the text. In addition, Appendix
R includes a tutorial on basic R commands as well as an
R time series tutorial.
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Książki
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