Premium
A new class of multivariate skew distributions with applications to bayesian regression models
Author(s) -
Sahu Sujit K.,
Dey Dipak K.,
Branco Márcia D.
Publication year - 2003
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3316064
Subject(s) - multivariate statistics , skewness , bayesian linear regression , bayesian multivariate linear regression , prior probability , mathematics , skew , bayesian probability , skew normal distribution , class (philosophy) , statistics , transformation (genetics) , regression analysis , econometrics , bayesian inference , computer science , artificial intelligence , telecommunications , biochemistry , chemistry , gene
The authors develop a new class of distributions by introducing skewness in multivariate elliptically symmetric distributions. The class, which is obtained by using transformation and conditioning, contains many standard families including the multivariate skew‐normal and t distributions. The authors obtain analytical forms of the densities and study distributional properties. They give practical applications in Bayesian regression models and results on the existence of the posterior distributions and moments under improper priors for the regression coefficients. They illustrate their methods using practical examples.