Applied Directional Statistics
Modern Methods and Case Studies
Christophe Ley editor Thomas Verdebout editor
Format:Paperback
Publisher:Taylor & Francis Ltd
Published:18th Dec '20
Currently unavailable, and unfortunately no date known when it will be back
This book collects important advances in methodology and data analysis for directional statistics. It is the companion book of the more theoretical treatment presented in Modern Directional Statistics (CRC Press, 2017). The field of directional statistics has received a lot of attention due to demands from disciplines such as life sciences or machine learning, the availability of massive data sets requiring adapted statistical techniques, and technological advances. This book covers important progress in bioinformatics, biology, astrophysics, oceanography, environmental sciences, earth sciences, machine learning and social sciences.
I would recommend Applied Directional Statistics to anyone who has a received graduate-level training in statistics and is interested in directional data. This book provides a wide variety of data examples that broadens readers’ horizon on the applicability of directional data. The methods described in this book are easy to follow and they all have connections with similar methods in Euclidean data. For instance, the directional kernel density estimator in Chapter 9 and 11 is closely related to the usual kernel density estimator in Euclidean space. These chapters serve as good reading references of a regular statistics course.
- Yen-Chi Chen, THE AMERICAN STATISTICIAN 2021, VOL. 75, NO. 3, 354
ISBN: 9780367733452
Dimensions: unknown
Weight: 580g
300 pages