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Missing data are common in medical and social science studies and often face a serious challenge in ultrahigh-dimensional data analysis. In this paper, a nonparametric feature screening approach based on the imputation technique is proposed for ultrahigh-dimensional data with responses missing at random, where the imputation technique is used to replacing each missing value with a set of plausible values. Our approach has many advantages. On one hand, the suggested method relies only on imputation, and its impact is considerably less than that of the feature screening procedure based on the inverse probability weighting approach in missing probabilities. On the other hand, our method does not rely on any model assumption and works generally for all kinds of situations. Simulation studies are conducted to examine the performance of our approach, and a real data example is also presented for illustration.

Nonparametric independence feature screening for ultrahigh-dimensional missing data