Time Series Analysis and Its Applications

With R Examples (Springer Texts in Statistics)

Robert H. Shumway Springer Science & Technology English Paperback 9783319524511

MRP: Rs 5000

Check Best Price

Add to Bookshelf

Store Name	Condition	Shipping	Go to Store
Find the best price for Time Series Analysis and Its Applications
Amazon.in	New Book	Free for Prime Members. Free for order of Rs 500+ (Otherwise Rs 40)	Buy Now
Flipkart.com	New Book	Free on order of Rs 500+ (Otherwise Rs 40)	Buy Now
Jiomart.com	New Book	Free Shipping	Buy Now
Bookscape.com	New Book	Free Shipping	Buy Now
Bookswagon.com	New Book	Rs 39 per book	Buy Now
Amazon.com	New Book	Free on order of USD 25.00	Buy Now

RELATED GENRES

Biology Textbooks

Biostatistics

Statistics

RELATED TAGS

Robert H. Books
Springer Books

AVAILABLE EDITIONS

9783319524511
Paperback
Rs 5000

B073H6HGJR
eBook

Complete Metadata
Publishing Group	:
Pages	:	562
Release Date	:	19 April 2017
Edition	:
Weight	:
Dimension	:
Sec Author/Narrator	:
Series Name	:
Book Awards	:
ISBN-10/SKU/ASIN	:	3319524518
BISAC Codes	:
Place of Origin	:
Main Distributor	:
Goodreads Page	:
Youtube Video	:
Author’s Website	:
Publisher’s Website	:

Most Popular Categories

Deals by Big Brands

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.

Robert H. Shumway, PhD, is Professor Emeritus of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He won the 1986 American Statistical Association Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center Statistics Award; both awards were for joint papers on time series applications. He is also the author of a Prentice-Hall text on applied time series analysis and served as a Departmental Editor for the Journal of Forecasting and Associate Editor for the Journal of the American Statistical Association.David S. Stoffer, PhD, is Professor of Statistics at the University of Pittsburgh. He is a Fellow of the American Statistical Association and has made seminal contributions to the analysis of categorical time series. David won the 1989 American Statistical Association Award for Outstanding Statistical Application in a joint paper analyzing categorical time series arising in infant sleep-state cycling. He is currently a Departmental Editor of the Journal of Forecasting and an Associate Editor of the Annals of Statistical Mathematics. He has served as Program Director in the Division of Mathematical Sciences at the National Science Foundation and as Associate Editor for the Journal of the American Statistical Association.