  Physical phenomena are commonly modeled by numerical simulators. Such codes
can take as input a high number of uncertain parameters and it is important to
identify their influences via a global sensitivity analysis (GSA). However,
these codes can be time consuming which prevents a GSA based on the classical
Sobol' indices, requiring too many simulations. This is especially true as the
number of inputs is important. To address this limitation, we consider recent
advances in dependence measures, focusing on the distance correlation and the
Hilbert-Schmidt independence criterion (HSIC). Our objective is to study these
indices and use them for a screening purpose. Numerical tests reveal some
differences between dependence measures and classical Sobol' indices, and
preliminary answers to "What sensitivity indices to what situation?" are
derived. Then, two approaches are proposed to use the dependence measures for a
screening purpose. The first one directly uses these indices with independence
tests; asymptotic tests and their spectral extensions exist and are detailed.
For a higher accuracy in presence of small samples, we propose a non-asymptotic
version based on bootstrap sampling. The second approach is based on a linear
model associating two simulations, which explains their output difference as a
weighed sum of their input differences. From this, a bootstrap method is
proposed for the selection of the influential inputs. We also propose a
heuristic approach for the calibration of the HSIC Lasso method. Numerical
experiments are performed and show the potential of these approaches for
screening when many inputs are not influential.
