NDLI: Dependence tree structure estimation via copula

Content Provider	Springer Nature Link
Author	Ma, Jian Sun, Zeng Qi Chen, Sheng Liu, Hong Hai
Copyright Year	2012
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.
Starting Page	113
Ending Page	121
Page Count	9
File Format	PDF
ISSN	14768186
Journal	International Journal of Automation and Computing
Volume Number	9
Issue Number	2
e-ISSN	17518520
Language	English
Publisher	Institute of Automation, Chinese Academy of Sciences
Publisher Date	2012-04-11
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	Copula empirical copula dependence tree structure learning probability distribution Computer-Aided Engineering (CAD, CAE) and Design Computer Applications Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Control and Systems Engineering Modeling and Simulation Computer Science Applications

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