Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics, sport statistics and the study of asymptotic approximations via Stein’s Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société Française de Statistique and an elected membership of the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis, Annals of the Institute of Statistical Mathematics, and Econometrics and Statistics. Thomas Verdebout is professor of mathematical statistics at Université libre de Bruxelles (ULB). His main research interests are semi-parametric statistics, high-dimensional statistics, directional statistics and rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an elected member of the International Statistical Institute. He is associate editor for the journals Statistics and Probability Letters and Journal of Multivariate Analysis.

• 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