  We describe here a new method to estimate copula measure. From N observations
of two variables X and Y, we draw a huge number m of subsamples (size n<N), and
we compute the joint ranks in these subsamples. Then, for each bivariate rank
(p,q) (0<p,q<n+1), we count the number of subsamples such that there exist an
observation of the subsample with bivariate rank (p,q). This counting gives an
estimate of the density of the copula. The simulation study shows that this
method seems to gives a better than the usual kernel method. The main advantage
of this new method is then we do not need to choose and justify the kernel. In
exchange, we have to choose a subsample size: this is in fact a problem very
similar to the bandwidth choice. We have then reduced the overall difficulty.
