SURE estimates for high dimensional classification

Abstract

AbstractIn the present paper, we consider the high dimensional classification problem, which has become much important in many modern statistical studies and applications. We develop new classifiers based on Fisher's linear classification rule and empirical Bayes. In particular, we propose to employ the Stein's unbiased risk estimate (SURE) to estimate the sparse or non‐sparse mean difference, which could be plugged into the linear classification rules. Using simulation studies under a variety of settings, we demonstrate that our classifiers perform well especially when the features are non‐sparse. We also illustrate the use of the new proposal to classification problems in some real data examples.