  The Shapley effects are global sensitivity indices: they quantify the impact
of each input variable on the output variable in a model. In this work, we
suggest new estimators of these sensitivity indices. When the input
distribution is known, we investigate the already existing estimator and
suggest a new one with a lower variance. Then, when the distribution of the
inputs is unknown, we extend these estimators. Finally, we provide asymptotic
properties of the estimators studied in this article.
