circular statistics; copulas; directional statistics; finite mixtures; heavy tails; skewness; transformation approach; Statistics and Probability; Statistics, Probability and Uncertainty
Abstract :
[en] Probability distributions are the building blocks of statistical modeling and inference. It is therefore of the utmost importance to know which distribution to use in what circumstances, as wrong choices will inevitably entail a biased analysis. In this article, we focus on circumstances involving complex data and describe the most popular flexible models for these settings. We focus on the following complex data: multivariate skew and heavy-tailed data, circular data, toroidal data, and cylindrical data. We illustrate the strength of flexible models on the basis of concrete examples and discuss major applications and challenges.
Disciplines :
Mathematics
Author, co-author :
LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) ; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
Babić, Slađana; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium ; Vlerick Business School, Brussels, Belgium
Craens, Domien; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
External co-authors :
yes
Language :
English
Title :
Flexible models for complex data with applications
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