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arXiv · 2009.05247

A simulation study of semiparametric estimation in copula models based on minimum Alpha-Divergence

Morteza Mohammadi·Mohammad Amini·Mahdi Emadi
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Abstract

The purpose of this paper is to introduce two semiparametric methods for the estimation of copula parameter. These methods are based on minimum Alpha-Divergence between a non-parametric estimation of copula density using local likelihood probit transformation method and a true copula density function. A Monte Carlo study is performed to measure the performance of these methods based on Hellinger distance and Neyman divergence as special cases of Alpha-Divergence. Simulation results are compared to the Maximum Pseudo-Likelihood (MPL) estimation as a conventional estimation method in well-known bivariate copula models. These results show that the proposed method based on Minimum Pseudo Hellinger Distance estimation has a good performance in small sample size and weak dependency situations. The parameter estimation methods are applied to a real data set in Hydrology.

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Morteza Mohammadi, Mohammad Amini, Mahdi Emadi. 2020-09-11. A simulation study of semiparametric estimation in copula models based on minimum Alpha-Divergence. https://doi.org/10.19139/soic-2310-5070-974

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