  In this paper, we introduce a new distribution regression model for
probability distributions. This model is based on a Reproducing Kernel Hilbert
Space (RKHS) regression framework, where universal kernels are built using
Wasserstein distances for distributions belonging to W 2 ($\Omega$) and
$\Omega$ is a compact subspace of R. We prove the universal kernel property of
such kernels and use this setting to perform regressions on functions.
Different regression models are first compared with the proposed one on
simulated functional data. We then apply our regression model to transient
evoked otoascoutic emission (TEOAE) distribution responses and real predictors
of the age.
