TY - JOUR
T1 - Analysis of directional dependence using asymmetric copula-based regression models
AU - Kim, Daeyoung
AU - Kim, Jong Min
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014/9
Y1 - 2014/9
N2 - The directional dependence between variables using asymmetric copula regression has drawn much attention in recent years. There are, however, some critical issues which have not been properly addressed in regards to the statistical inference of the directional dependence. For example, the previous use of asymmetric copulas failed to fully capture the dependence patterns between variables, and the method used for the parameter estimation was not optimal. In addition, no method was considered for selecting a suitable asymmetric copula or for computing the general measurements of the directional dependence when there are no closed-form expressions. In this paper, we propose a generalized multiple-step procedure for the full inference of the directional dependence in joint behaviour based on the asymmetric copula regression. The proposed procedure utilizes several novel methodologies that have not been considered in the literature of the analysis of directional dependence. The performance and advantages of the proposed procedure are illustrated using two real data examples, one from biological research on histone genes, and the other from developmental research on attention deficit hyperactivity disorder.
AB - The directional dependence between variables using asymmetric copula regression has drawn much attention in recent years. There are, however, some critical issues which have not been properly addressed in regards to the statistical inference of the directional dependence. For example, the previous use of asymmetric copulas failed to fully capture the dependence patterns between variables, and the method used for the parameter estimation was not optimal. In addition, no method was considered for selecting a suitable asymmetric copula or for computing the general measurements of the directional dependence when there are no closed-form expressions. In this paper, we propose a generalized multiple-step procedure for the full inference of the directional dependence in joint behaviour based on the asymmetric copula regression. The proposed procedure utilizes several novel methodologies that have not been considered in the literature of the analysis of directional dependence. The performance and advantages of the proposed procedure are illustrated using two real data examples, one from biological research on histone genes, and the other from developmental research on attention deficit hyperactivity disorder.
KW - asymmetric copula
KW - directional dependence
KW - regression function
UR - http://www.scopus.com/inward/record.url?scp=84900559341&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84900559341&partnerID=8YFLogxK
U2 - 10.1080/00949655.2013.779696
DO - 10.1080/00949655.2013.779696
M3 - Article
AN - SCOPUS:84900559341
SN - 0094-9655
VL - 84
SP - 1990
EP - 2010
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
IS - 9
ER -