商品簡介
Directional data naturally arises in numerous and diverse domains such as earth sciences (in particular geology), meteorology, astronomy, studies of animal behavior, image analysis, neurosciences, medicine, machine learning, bioinformatics, and cosmology. The difficulty inherent to directional data compared to usual multivariate data lies in the fact that they take values on non-linear manifolds, rendering their analysis via standard multivariate methods impossible. This book provides a detailed account on some of the newest methods for dealing with this type of data.
作者簡介
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.
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