[en] Skew-symmetric distributions are a popular family of flexible distributions that conveniently model non-normal features such as skewness, kurtosis and multimodality. Unfortunately, their frequentist inference poses several difficulties, which may be adequately addressed by means of a Bayesian approach. This paper reviews the main prior distributions proposed for the parameters of skew-symmetric distributions, with special emphasis on the skew-normal and the skew-t distributions which are the most prominent skew-symmetric models. The paper focuses on the univariate case in the absence of covariates, but more general models are also discussed.
Disciplines :
Mathematics Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Ghaderinezhad, Fatemeh; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
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
Loperfido, Nicola; Dipartimento di Economia, Società e Politica, Università degli Studi di Urbino Carlo Bo, Urbino (PU), Italy
External co-authors :
yes
Language :
English
Title :
Bayesian inference for skew-symmetric distributions
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