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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, pp. 113-121. (doi:10.1007/s11633-012-0624-6).

Record type: Article

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.

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More information

Published date: April 2012
Keywords: copula, empirical copula, dependence, tree structure learning, probability distribution
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 334890
URI: http://eprints.soton.ac.uk/id/eprint/334890
ISSN: 1476-8186
PURE UUID: 536e1195-e403-43b1-b900-d72818b4e97d

Catalogue record

Date deposited: 08 Mar 2012 11:19
Last modified: 18 Jul 2017 06:11

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Contributors

Author: Jian Ma
Author: Zeng-Qi Sun
Author: Sheng Chen
Author: Hong-Hai Liu

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