ON A WAVELET-BASED METHOD FOR ESTIMATING THE COPULA FUNCTION

Using both the Wiener chaos expansion on the systemic economic factor and a Gaussian ap- proximation on the associated truncated loss, we significantly reduce the computational

This is due to the fact that the nonparametric procedure tries to produce a smooth copula estimate both within and outside the unit square (where the copula vanishes). Once the

Keywords and phrases: Nonparametric regression, Derivatives function estimation, Wavelets, Besov balls, Hard

(4) This assumption controls the decay of the Fourier coefficients of g, and thus the smoothness of g. It is a standard hypothesis usually adopted in the field of

For the case where d = 0 and the blurring function is ordinary-smooth or super-smooth, [22, 23, Theorems 1 and 2] provide the upper and lower bounds of the MISE over Besov balls for

Keywords and phrases: Nonparametric regression, Biased data, Deriva- tives function estimation, Wavelets, Besov balls..

This part is not exactly an illustration of the theoretical part since it does not focus on the separating rate between the alternative and the null hypothesis, but it is devoted to

A note on the adaptive estimation of a bi-dimensional density in the case of knowledge of the copula density.. Ingo Bulla, Christophe Chesneau, Fabien Navarro,