A Simulation Study of Semiparametric Estimation in Copula Models Based on Minimum Alpha-Divergence

  • Morteza Mohammadi Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad 91775, Iran
  • Mohammad Amini Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad 91775, Iran.
  • Mahdi Emadi Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad 91775, Iran.
Keywords: Alpha-Divergence; Copula Density; Hellinger Distance; Semiparametric Estimation.


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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How to Cite
Mohammadi, M., Amini, M., & Emadi, M. (2020). A Simulation Study of Semiparametric Estimation in Copula Models Based on Minimum Alpha-Divergence. Statistics, Optimization & Information Computing, 8(4), 834-845. https://doi.org/10.19139/soic-2310-5070-974
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