Model free feature screening for ultrahigh dimensional data with responses missing at random

The paper concerns the feature screening for the ultrahigh dimensional data with responses missing at random. A model free feature screening procedure based on the inverse probability weighted methods has been proposed, where the Kolmogorov filter method is used to screen the important features under an unknown propensity score function. The suggested screening procedure has several desirable advantages. First, it has property of robust to heavy-tailed distributions of predictors and the presence of potential outliers. Second, it is a model free procedure with mild model assumptions. Third, it can deal with the missing data problem with responses missing at random. Monte Carlo simulation studies are conducted to examine the performance of the proposed procedure and a real data application is also conducted to evaluate and illustrate the proposed methods.