Dependence Measure for non-additive model
This work addresses the need for a robust and versatile dependence measure in statistical analysis, though it appears incremental as it builds on existing copula-based methods.
The authors tackled the problem of measuring statistical dependence between variables by proposing the Copula Dependency Coefficient (CDC), which is robust to outliers, easy to implement, and effective for high-dimensional data, showing it can detect dependence in both additive and non-additive models.
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.