Times series analysis and its applications: With R examples (Record no. 891)

MARC details
000 -LEADER
fixed length control field 02309nam a2200253 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250411115050.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230623b2017|||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319524511
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5 S58, 4
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Shumway, Robert H.
Relator term Author
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Stoffer, David S.
Relator term Co-Author
245 ## - TITLE STATEMENT
Title Times series analysis and its applications: With R examples
250 ## - EDITION STATEMENT
Edition statement 4th ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication USA:
Name of publisher Springer,
Year of publication 2017.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xiii, 562p.; 21cms.
490 ## - SERIES STATEMENT
Series statement Springer texts in statistics
500 ## - GENERAL NOTE
General note The fourth edition of this popular graduate textbook, like its predecessors, 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.<br/><br/>The book is designed as a textbook 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, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods.<br/><br/>This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Time-series analysis
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Time-series analysis
General subdivision Data processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term R (Computer program language)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Statistics
General subdivision Mathematical statistics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Not for loan Permanent Location Current Location Shelving location Full call number Accession Number Koha item type
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