Probit Transformation for Nonparametric Kernel Estimation of the Copula Density

@inproceedings{Geenens2014ProbitTF,
  title={Probit Transformation for Nonparametric Kernel Estimation of the Copula Density},
  author={Gery Geenens and Arthur Charpentier},
  year={2014}
}
Copula modelling has become ubiquitous in modern statistics. Here, the problem of nonparametrically estimating a copula density is addressed. Arguably the most popular nonparametric density estimator, the kernel estimator is not suitable for the unit-square-supported copula densities, mainly because it is heavily a↵ected by boundary bias issues. In addition, most common copulas admit unbounded densities, and kernel methods are not consistent in that case. In this paper, a kernel-type copula… CONTINUE READING