  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.
