  Feature screening is useful and popular to detect informative predictors for
ultrahigh-dimensional data before developing proceeding statistical analysis or
constructing statistical models. While a large body of feature screening
procedures has been developed, most of them are restricted on examining either
continuous or discrete responses. Moreover, even though many model-free feature
screening methods have been proposed, additional assumptions are imposed in
those methods to ensure their theoretical results. To address those
difficulties and provide simple implementation, in this paper we extend the
rank-based coefficient of correlation proposed by Chatterjee (2020) to develop
feature screening procedure. We show that this new screening criterion is able
to deal with continuous and discrete responses. Theoretically, sure screening
property is established to justify the proposed method. Simulation studies
demonstrate that the predictors with nonlinear and oscillatory trajectory are
successfully detected regardless of the distribution of the response.
