Dependence Measure for non-additive model
Machine Learning
2018-03-28 v5
Abstract
We proposed a new statistical dependency measure called Copula Dependency Coefficient(CDC) for two sets of variables based on copula. It is robust to outliers, easy to implement, powerful and appropriate to high-dimensional variables. These properties are important in many applications. Experimental results show that CDC can detect the dependence between variables in both additive and non-additive models.
Cite
@article{arxiv.1310.1562,
title = {Dependence Measure for non-additive model},
author = {Hangjin Jiang and Yiming Ding},
journal= {arXiv preprint arXiv:1310.1562},
year = {2018}
}
Comments
This paper has been withdrawn by the author due to change of the main content