同济大学数学系学术报告
题 目：How to make model-free feature screening approaches for full data applicable to missing response case?
报告人：王启华 研究员
(中科院，杰青，长江学者）
【摘要】It is quite challenge to develop model-free feature screening approaches directly for missing response problems since the existing standard missing data analysis methods cannot be applied directly to high dimensional case. This paper develops a novel technique by borrowing information of missingness indicators such that any feature screening procedures for ultrahigh-dimensional covariates with full data can be applied to missing response case. This technique is developed by proving that the joint set of the active predictors on the response and missingness indicator equals to the set of the active predictors on the product of the response and missingness indicator. Then, standard model-free feature screening procedures for full data can be applied to estimating the set of the active predictors on the product of the response and the indicator and we propose two methods to estimate the set of the active predictors on the response. A simulation study was conducted to compare the proposed methods with the ``complete case" (CC) approach. Real data analysis was used to illustrate the proposed method. Both the simulation studies and real data analysis indicate that the proposed method outperforms the CC method.
时间：2014年12月17日（周三）上午10:10开始
地点：数学系致远楼107会议室
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