Strong Sure Screening of Ultra-high Dimensional Categorical Data

Abstract: Feature screening for ultra high dimensional feature spaces plays a critical role in the analysis of data sets whose predictors exponentially exceed the number of observations. Such data sets are becoming increasingly prevalent in areas such as bioinformatics, medical imaging, and social network analysis. Frequently, these data sets have both categorical response and categorical covariates, yet extant feature screening literature rarely considers such data types. We propose a new screening procedure rooted in the Cochran-Armitage trend test. Our method is specifically applicable for data where both the response and predictors are categorical. Under a set of reasonable conditions, we demonstrate that our screening procedure has the strong sure screening property, which extends the seminal results of Fan and Lv. A series of four simulations are used to investigate the performance of our method relative to three other screening methods. We also apply a two-stage iterative approach to a real data example by first employing our proposed method, and then further screening a subset of selected covariates using lasso, adaptive-lasso and elastic net regularization.