Abstract

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.

摘要

缺失数据在医学和社会科学研究中很常见，在超高维数据分析中经常面临严峻挑战。在本文中，针对随机缺失响应的超高维数据，提出了一种基于插补技术的非参数特征筛选方法，其中插补技术用于将每个缺失值替换为一组似是而非的值。我们的方法有很多优点。一方面，建议的方法仅依赖于插补，其影响远小于基于缺失概率的逆概率加权方法的特征筛选过程。另一方面，我们的方法不依赖于任何模型假设，并且通常适用于各种情况。进行模拟研究以检查我们方法的性能，