Models for Dependent Time Series

· ·
· CRC Press
Ebook
340
Pages
Eligible
Ratings and reviews aren’t verified  Learn More

About this ebook

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vect

About the author

Granville Tunnicliffe Wilson is a reader emeritus in the Department of Mathematics and Statistics at Lancaster University, UK. His research focuses on methodology and software for time series modeling and prediction.

Marco Reale is an associate professor in the School of Mathematics and Statistics at the University of Canterbury, New Zealand. His research interests include time series analysis, statistical learning, and stochastic optimization.

John Haywood is a senior lecturer in the School of Mathematics and Statistics at Victoria University of Wellington, New Zealand. His research interests include time series analysis, seasonal modeling, and statistical applications, particularly in ecology.

Rate this ebook

Tell us what you think.

Reading information

Smartphones and tablets
Install the Google Play Books app for Android and iPad/iPhone. It syncs automatically with your account and allows you to read online or offline wherever you are.
Laptops and computers
You can listen to audiobooks purchased on Google Play using your computer's web browser.
eReaders and other devices
To read on e-ink devices like Kobo eReaders, you'll need to download a file and transfer it to your device. Follow the detailed Help Center instructions to transfer the files to supported eReaders.