國立高雄大學圖資館 |

Language: English

Back

Time series analysis and its applica...

Shumway, Robert H.

Time series analysis and its applicationswith R examples /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time series analysis and its applicationsby Robert H. Shumway, David S. Stoffer.
Reminder of title:	with R examples /
Author:	Shumway, Robert H.
other author:	Stoffer, David S.
Published:	Cham :Springer International Publishing :2017.
Description:	xiii, 562 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Time-series analysis.
Online resource:	https://doi.org/10.1007/978-3-319-52452-8
ISBN:	9783319524528$q(electronic bk.)

Time series analysis and its applicationswith R examples /
Shumway, Robert H.

Time series analysis and its applicationswith R examples /[electronic resource] :by Robert H. Shumway, David S. Stoffer. - 4th ed. - Cham :Springer International Publishing :2017. - xiii, 562 p. :ill. (some col.), digital ;24 cm. - Springer texts in statistics,1431-875X. - Springer texts in statistics..

1. Characteristics of Time Series -- 2. Time Series Regression and Exploratory Data Analysis -- 3. ARIMA Models -- 4. Spectral Analysis and Filtering -- 5. Additional Time Domain Topics -- 6. State-Space Models -- 7. Statistical Methods in the Frequency Domain -- 8. Appendix A: Large Sample Theory -- Appendix B: Time Domain Theory -- Appendix C: Spectral Domain Theory -- Appendix R: R Supplement.

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. 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. 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. Student-tested and improved Accessible and complete treatment of modern time series analysis Promotes understanding of theoretical concepts by bringing them into a more practical context Comprehensive appendices covering the necessities of understanding the mathematics of time series analysis Instructor's Manual available for adopters New to this edition: Introductions to each chapter replaced with one-page abstracts All graphics and plots redone and made uniform in style Bayesian section completely rewritten, covering linear Gaussian state space models only R code for each example provided directly in the text for ease of data analysis replication Expanded appendices with tutorials containing basic R and R time series commands Data sets and additional R scripts available for download on Springer.com Internal online links to every reference (equations, examples, chapters, etc.)

ISBN: 9783319524528$q(electronic bk.)

Standard No.: 10.1007/978-3-319-52452-8doiSubjects--Topical Terms:

181890
Time-series analysis.

LC Class. No.: QA280 / .S585 2017

Dewey Class. No.: 519.5

Time series analysis and its applicationswith R examples /
LDR:03898nmm a2200349 a 4500 001 558436
003 DE-He213
005 20191112142841.0
006 m d
007 cr nn 008maaau
008 191212s2017 gw s 0 eng d
020 $a 9783319524528$q(electronic bk.)
020 $a 9783319524511$q(paper)
024 7 $a 10.1007/978-3-319-52452-8 $2 doi
035 $a 978-3-319-52452-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA280 $b .S585 2017
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA280 $b .S562 2017
100 1 $a Shumway, Robert H. $3 208678
245 1 0 $a Time series analysis and its applications $h [electronic resource] : $b with R examples / $c by Robert H. Shumway, David S. Stoffer.
250 $a 4th ed.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xiii, 562 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 1431-875X
505 0 $a 1. Characteristics of Time Series -- 2. Time Series Regression and Exploratory Data Analysis -- 3. ARIMA Models -- 4. Spectral Analysis and Filtering -- 5. Additional Time Domain Topics -- 6. State-Space Models -- 7. Statistical Methods in the Frequency Domain -- 8. Appendix A: Large Sample Theory -- Appendix B: Time Domain Theory -- Appendix C: Spectral Domain Theory -- Appendix R: R Supplement.
520 $a 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. 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. 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. Student-tested and improved Accessible and complete treatment of modern time series analysis Promotes understanding of theoretical concepts by bringing them into a more practical context Comprehensive appendices covering the necessities of understanding the mathematics of time series analysis Instructor's Manual available for adopters New to this edition: Introductions to each chapter replaced with one-page abstracts All graphics and plots redone and made uniform in style Bayesian section completely rewritten, covering linear Gaussian state space models only R code for each example provided directly in the text for ease of data analysis replication Expanded appendices with tutorials containing basic R and R time series commands Data sets and additional R scripts available for download on Springer.com Internal online links to every reference (equations, examples, chapters, etc.)
650 0 $a Time-series analysis. $3 181890
650 0 $a Time-series analysis $x Data processing. $3 190935
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
700 1 $a Stoffer, David S. $3 208677
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer texts in statistics. $3 559235
856 4 0 $u https://doi.org/10.1007/978-3-319-52452-8
950 $a Mathematics and Statistics (Springer-11649)