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    Flexible models for complex data with applications

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    Publication type
    Journal article with impact factor
    Author
    Ley, Christophe
    Babic, Sladana
    Craens, Domien
    Publication Year
    2021
    Journal
    Annual Review of Statistics and Its Application
    Publication Volume
    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.
    Keyword
    Distribution
    Knowledge Domain/Industry
    Accounting & Finance
    DOI
    10.1146/annurev-statistics-040720-025210
    URI
    http://hdl.handle.net/20.500.12127/6646
    ae974a485f413a2113503eed53cd6c53
    10.1146/annurev-statistics-040720-025210
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