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Zusatztext "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 Papers Informationen zum Autor 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 Société Française 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 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 . Klappentext This book provides a detailed account on some of the newest methods for dealing with directional data. Directional data naturally arises in diverse domains such as earth sciences (in particular geology), meteorology, astronomy, studies of animal behavior, image analysis, neurosciences, medicine, machine learning, bioinformatics, and cosmology. Zusammenfassung This book provides a detailed account on some of the newest methods for dealing with directional data. Directional data naturally arises in diverse domains such as earth sciences (in particular geology), meteorology, astronomy, studies of animal behavior, image analysis, neurosciences, medicine, machine learning, bioinformatics, and cosmology. Inhaltsverzeichnis 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. ...
List of contents
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.
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"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 Papers
