scholarly journals A Skew-normal copula-driven GLMM

2016 ◽  
Vol 70 (4) ◽  
pp. 396-413 ◽  
Author(s):  
Kalyan Das ◽  
Mohamad Elmasri ◽  
Arusharka Sen
Keyword(s):  
Normal Copula ◽  
Skew Normal

scholarly journals Copulaesque Versions of the Skew-Normal and Skew-Student Distributions

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 815
Author(s):  
Christopher Adcock
Keyword(s):  
Distribution Function ◽  
Normal Distribution ◽  
Degrees Of Freedom ◽  
Special Functions ◽  
Normal Distribution Function ◽  
Skew Normal Distribution ◽  
Marginal Distributions ◽  
Student’S T ◽  
Normal Copula ◽  
Skew Normal

A recent paper presents an extension of the skew-normal distribution which is a copula. Under this model, the standardized marginal distributions are standard normal. The copula itself depends on the familiar skewing construction based on the normal distribution function. This paper is concerned with two topics. First, the paper presents a number of extensions of the skew-normal copula. Notably these include a case in which the standardized marginal distributions are Student’s t, with different degrees of freedom allowed for each margin. In this case the skewing function need not be the distribution function for Student’s t, but can depend on certain of the special functions. Secondly, several multivariate versions of the skew-normal copula model are presented. The paper contains several illustrative examples.

scholarly journals Multivariate Skew Normal Copula for Non-exchangeable Dependence

2016 ◽  
Vol 91 ◽  
pp. 141-150 ◽  
Author(s):  
Zheng Wei ◽  
Seongyong Kim ◽  
Daeyoung Kim
Keyword(s):  
Normal Copula ◽  
Skew Normal

Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application

2019 ◽  
Vol 18 (01) ◽  
pp. 365-387 ◽  
Author(s):  
Zheng Wei ◽  
Seongyong Kim ◽  
Boseung Choi ◽  
Daeyoung Kim
Keyword(s):  
Convergence Property ◽  
Real Data ◽  
Radial Symmetry ◽  
High Dimensional ◽  
Dependence Structure ◽  
Data Set ◽  
Asymmetric Dependence ◽  
Identical Manner ◽  
Normal Copula ◽  
Skew Normal

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.

The Skew-Normal and Related Families

2009 ◽  
Author(s):  
Adelchi Azzalini ◽  
Antonella Capitanio
Keyword(s):  
Skew Normal
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