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.
Bioinformatics. State-space Markov processes applied to sea waves. Needlets and cosmology. Machine learning. Human body measurements. Circular statistics with R.
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