Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer


Li-Ping Zhu

East China Normal University

Dimension reduction in ultrahigh dimensional feature space characterizes various contemporary problems in scientific discoveries. In this talk, we propose a model-free independence screening procedure to select the subset of active predictors by using the diagonal elements of an average partial mean estimation matrix. The new proposal possesses the sure independence screening property for a wide range of semi-parametric regressions, i.e. it guarantees to select the subset of active predictors with probability approaching to one as the sample size diverges. In addition, it is computationally efficient in the sense that it is free of tuning and avoids completely iterative algorithm. By adding a series of auxiliary variables to set up a benchmark for screening, a new technique is introduced to reduce the false discovery rate in the feature screening stage. Numerical studies through several synthetic examples and a real data example are presented to illustrate the methodology. The empirical investigations found that the new proposal allows strong correlations within the group of inactive features, and works properly even when the number of active predictors is fairly large.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.