
Modern Directional Statistics
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 15. September 2017
Book
Hardback
190 pages
978-1-4987-0664-3 (ISBN)
Description
Modern Directional Statistics collects important advances in methodology and theory for directional statistics over the last two decades. It provides a detailed overview and analysis of recent results that can help both researchers and practitioners. Knowledge of multivariate statistics eases the reading but is not mandatory.
The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods.
Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein's Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Societe Francaise de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics.
Thomas Verdebout is professor of mathematical statistics at Universite 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.
The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods.
Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein's Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Societe Francaise de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics.
Thomas Verdebout is professor of mathematical statistics at Universite 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.
Reviews / Votes
"The book is definitely handy for researchers and graduate students in statistics as well as for scientists and practical users in bioscience, ecological and environmental sciences, social sciences and other applied areas where directional data analysis is needed and even high-dimensional data analytics is encountered." ~Shuangzhe Liu, Stat PapersMore details
Series
Language
English
Place of publication
Oxford
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 15 mm
Weight
567 gr
ISBN-13
978-1-4987-0664-3 (9781498706643)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Christophe Ley | Thomas Verdebout
Modern Directional Statistics
Book
06/2020
1st Edition
Chapman & Hall/CRC
€72.20
Shipment within 15-20 days

Christophe Ley | Thomas Verdebout
Modern Directional Statistics
E-Book
08/2017
1st Edition
Chapman & Hall/CRC
€68.49
Available for download

Christophe Ley | Thomas Verdebout
Modern Directional Statistics
E-Book
08/2017
Chapman & Hall/CRC
€68.49
Available for download
Persons
Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein's Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Societe Francaise de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics.
Thomas Verdebout is professor of mathematical statistics at Universite 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.
Thomas Verdebout is professor of mathematical statistics at Universite 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.
Content
Advances in flexible parametric distribution theory. Advances in kernel density estimation on directional supports. Computational and graphical methods. Local asymptotic normality for directional data. Recent results for tests of uniformity and symmetry. High-dimensional directional statistics.