Dependence tree structure estimation via copula

Ma, Jian, Sun, Zeng-Qi, Chen, Sheng and Liu, Hong-Hai (2012) Dependence tree structure estimation via copula. International Journal of Automation and Computing, 9, (2), Spring Issue, 113-121. (doi:10.1007/s11633-012-0624-6).


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We propose an approach for dependence tree structure learning via copula. A nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is proposed to estimate maximum spanning bivariate copula associated with bivariate dependence relations. The main advantage of the approach is that learning with empirical copula focuses on dependence relations among random variables, without the need to know the properties of individual variables as well as without the requirement to specify parametric family of entire underlying distribution for individual variables. Experiments on two real-application data sets show the effectiveness of the proposed method.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1007/s11633-012-0624-6
ISSNs: 1476-8186 (print)
1751-8520 (electronic)
Keywords: copula, empirical copula, dependence, tree structure learning, probability distribution
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
ePrint ID: 334890
Accepted Date and Publication Date:
April 2012Published
Date Deposited: 08 Mar 2012 11:19
Last Modified: 31 Mar 2016 14:24
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