A quantile-copula approach to conditional density estimation and applications to prediction

Résumé

We present a new non-parametric estimator of the conditional density of the kernel type. It is based on an efficient transformation of the data by quantile transform. By use of the copula representation, it turns out to have a remarkable product form.We study its asymptotic properties and compare its bias and variance to competitors based on nonparametric regression. A comparative numerical simulation is provided.