In 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.

中文翻译：

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对高维分类的肯定估计

在本文中，我们考虑了高维分类问题，它在许多现代统计研究和应用中已变得非常重要。我们基于Fisher线性分类规则和经验贝叶斯开发了新的分类器。特别是，我们建议采用Stein的无偏风险估计（SURE）来估计稀疏或非稀疏平均差，可以将其插入线性分类规则中。通过在各种设置下进行仿真研究，我们证明了分类器的性能很好，尤其是在特征不稀疏的情况下。我们还将说明在一些实际数据示例中如何使用新建议对问题进行分类。