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Bivariate Dependency Analysis using Jeffrey and Hellinger Divergence Measures based on Copula Density Estimation by Improved Probit Transformation
Morteza Mohammadi, Mahdi Emadi , Mohammad Amini
Abstract:   (563 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.
Keywords: Divergence, Dependency, Copula Density, Probit Transformation.
Full-Text [PDF 6982 kb]   (8 Downloads)    
Type of Study: Applied | Subject: Statistical Inference
Received: 2020/01/14 | Accepted: 2020/06/27 | Published: 2021/02/28
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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