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.
|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 > Comms, Signal Processing & Control
|Date Deposited:||08 Mar 2012 11:19|
|Last Modified:||27 Mar 2014 20:19|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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