Dependence tree structure estimation via copula

Authors:
Jian Ma;Zeng-Qi Sun;Sheng Chen;Hong-Hai Liu
Affiliations:
Department of Computer Science, Tsinghua University, Beijing, PRC 100084;Department of Computer Science, Tsinghua University, Beijing, PRC 100084;Electronics and Computer Science, Faculty of Physical and Applied Sciences, University of Southampton, Southampton, UK SO17 1BJ and Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi ...;Intelligent Systems and Robotics Research Group, School of Creative Technologies, University of Portsmouth, Portsmouth, UK PO1 2DJ
Venue:
International Journal of Automation and Computing
Year:
2012

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Abstract

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.