Dimension reduction is a crucial issue for high-dimensional data analysis. When the correlation among the variables is strong, the original SIRS (Zhu et al. in J Am Stat Assoc 106(496):1464–1475, 2011) may lose efficiency. Under high-dimensional setting, eliminating the bad influence caused by the correlation has become an important issue. Aiming at this issue, we propose a feature screening approach by combining the marginal empirical likelihood with the conditional SIRS. Based on a centralized SIRS, the correlation among the variables is significantly reduced and consequently, the related empirical likelihood is improved remarkably. Moreover, our method is model-free due to the properties of SIRS and empirical likelihood. The proposed method can select important predictors directly without parameter estimation, implying that the method is computationally simple. Under some general conditions, the proposed marginal empirical likelihood ratio is self-studentized. The simulation study shows that compared with other unconditional and conditional methods, our method is competitive and has a great superiority.

中文翻译：

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基于边际经验似然的非参数和半参数模型的条件 SIRS

降维是高维数据分析的关键问题。当变量之间的相关性很强时，原始 SIRS（Zhu 等人在 J Am Stat Assoc 106(496):1464–1475, 2011 中）可能会失去效率。在高维设置下，消除相关性带来的不良影响成为一个重要的问题。针对这个问题，我们提出了一种将边际经验似然与条件 SIRS 相结合的特征筛选方法。基于集中的SIRS，变量之间的相关性显着降低，因此相关的经验似然显着提高。此外，由于 SIRS 和经验似然的特性，我们的方法是无模型的。所提出的方法可以直接选择重要的预测变量，无需参数估计，这意味着该方法在计算上很简单。在某些一般条件下，建议的边际经验似然比是自学的。仿真研究表明，与其他无条件和条件方法相比，我们的方法具有竞争力，具有很大的优势。