Year: 2019
Type: preprint
Abstract: Discovering associations is of central importance in scientific practices. Currently, most researches consider only linear association measured by correlation coefficient, which has its theoretical limitations. In this paper, we propose a new method for discovering association with copula entropy -- a universal applicable association measure for not only linear cases, but nonlinear cases. The advantage of the method based on copula entropy over traditional method is demonstrated on the NHANES data by discovering more biomedical meaningful associations.
Source: arXiv (Cornell University)
Author: Ma, Jian
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2966852148