Overview
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.
Full Product Details
Author: Christophe Ley ,
Thomas Verdebout
Publisher: Taylor & Francis Ltd
Imprint: CRC Press
Weight: 0.660kg
ISBN: 9781138626430
ISBN 10: 1138626430
Pages: 300
Publication Date: 10 September 2018
Audience:
College/higher education
,
General/trade
,
Tertiary & Higher Education
,
General
Format: Hardback
Publisher's Status: Active
Availability: In Print
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Reviews
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
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
Author Information
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.
