Bivariate Dependency Analysis using Jeffrey and Hellinger Divergence Measures based on Copula Density Estimation by Improved Probit Transformation
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Morteza Mohammadi , Mahdi Emadi * , Mohammad Amini |
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Abstract: (3169 Views) |
Divergence measures can be considered as criteria for analyzing the dependency and can be rewritten based on the copula density function. In this paper, Jeffrey and Hellinger dependency criteria are estimated using the improved probit transformation method, and their asymptotic consistency is proved. In addition, a simulation study is performed to measure the accuracy of the estimators. The simulation results show that for low sample size or weak dependence, the Hellinger dependency criterion performs better than Kullback-Libeler and Jeffrey dependency criteria. Finally, the application of the studied methods in hydrology is presented. |
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Keywords: Divergence, Dependency, Copula Density, Probit Transformation. |
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Full-Text [PDF 405 kb]
(1034 Downloads)
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Type of Study: Applied |
Subject:
Statistical Inference Received: 2020/01/14 | Accepted: 2020/06/27 | Published: 2021/02/28
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