1st Edition

Applied Directional Statistics
Modern Methods and Case Studies

ISBN 9781138626430
Published September 10, 2018 by Chapman and Hall/CRC
300 Pages

USD $140.00

Prices & shipping based on shipping country


Book Description

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.

Table of Contents


Christophe Ley and Thomas Verdebout

1. Directional Statistics in Protein Bioinformatics

Kanti V. Mardia, Jesper Illemann Foldager, and Jes Frellsen

2. Statistics of Orientations of Symmetrical Objects

Richard Arnold and Peter Jupp

3. Correlated Cylindrical Data

Francesco Lagona

4. Toroidal Diffusions and Protein Structure Evolution

Eduardo García-Portugués, Michael Golden, Michael Soerensen, Kanti V. Mardia, Thomas Hamelryck, Jotun Hein

5. Noisy Directional Data

Thanh Mai Pham Ngoc

6. On Modelling of SE(3) Objects

Louis-Paul Rivest and Karim Oualkacha

7. Spatial and Spatio-temporal Circular Processes with Application to Wave Direction

Giovanna Jona-Lasinio, Alan E. Gelfand, and Gianluca Mastrantonio

8. Cylindrical Distributions and their Applications to Biological Data

Toshihiro Abe & Ichiro Ken Shimatani

9. Directional Statistics for Wildfires

Jose Ameijeiras-Alonso, Rosa M. Crujeiras, Alberto Rodríguez Casal

10. Bayesian Analysis of Circular Data in Social and Behavioural Sciences

Irene Klugkist, Jolien Cremers, and Kees Mulder

11. Nonparametric Classification for Circular Data

Marco Di Marzio, Stefania Fensore, and Charles C. Taylor

12. Directional Statistics in Machine Learning: A Brief Review

Suvrit Sra

13. Applied Directional Statistics with R: an Overview

Arthur Pewsey



View More



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.