Flexible models for complex data with applications
Ley, Christophe Babic, Sladana Craens, Domien
Ley, Christophe
Babic, Sladana
Craens, Domien
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Publication Year
2021
Journal
Annual Review of Statistics and Its Application
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8
Publication Issue
March
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Abstract
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.
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