Title
Multivariate Skew Normal Copula For Asymmetric Dependence: Estimation And Application
Abstract
The exchangeability and radial symmetry assumptions on the dependence structure of the multivariate data are restrictive in practical situations where the variables of interest are not likely to be associated to each other in an identical manner. In this paper, we propose a flexible class of multivariate skew normal copulas to model high-dimensional asymmetric dependence patterns. The proposed copulas have two sets of parameters capturing asymmetric dependence, one for association between the variables and the other for skewness of the variables. In order to efficiently estimate the two sets of parameters, we introduce the block coordinate ascent algorithm and discuss its convergence property. The proposed class of multivariate skew normal copulas is illustrated using a real data set.
Year
DOI
Venue
2019
10.1142/S021962201750047X
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Keywords
Field
DocType
Copula, non-exchangeability, radial asymmetry, skew-normal distribution
Convergence (routing),Applied mathematics,Data mining,Skew normal distribution,Skewness,Copula (linguistics),Multivariate statistics,Symmetry in biology,Skew,Mathematics
Journal
Volume
Issue
ISSN
18
1
0219-6220
Citations 
PageRank 
References 
0
0.34
11
Authors
4
Name
Order
Citations
PageRank
Zheng Wei111.37
Seongyong Kim232.06
Boseung Choi300.68
Daeyoung Kim4203.54