東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Time series analysis and its applica...

Shumway, Robert H.

Linked to FindBook

Google Book

Amazon

博客來

Time series analysis and its applications = with R examples /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time series analysis and its applications/ by 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.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Time-series analysis. -
Online resource:	https://doi.org/10.1007/978-3-319-52452-8
ISBN:	9783319524528

Time series analysis and its applications = with 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

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

532530
Time-series analysis.

LC Class. No.: QA280 / .S585 2017

Dewey Class. No.: 519.5

Time series analysis and its applications = with R examples /
LDR:03898nmm a2200349 a 4500 001 2190955
003 DE-He213
005 20191112142841.0
006 m d
007 cr nn 008maaau
008 200504s2017 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 731100
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 532530
650 0 $a Time-series analysis $x Data processing. $3 700459
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 891086
700 1 $a Stoffer, David S. $3 731099
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer texts in statistics. $3 1567152
856 4 0 $u https://doi.org/10.1007/978-3-319-52452-8
950 $a Mathematics and Statistics (Springer-11649)