论文标题
SMLE：在超高维GLM中进行联合特征筛选的R包装
SMLE: An R Package for Joint Feature Screening in Ultrahigh-dimensional GLMs
论文作者
论文摘要
受稀疏性的最大似然估计器（SMLE）在超高维回归中受到了特征筛选的关注。 SMLE是一种计算方便的方法，在筛选过程中自然融合了特征之间的关节效应。我们开发了一个公开可用的R软件包SMLE，该软件包提供了一个用户友好的环境，可以在广义线性模型中执行SMLE。特别是，该软件包包括使用SMLE进行SMLE进行SMLE筛查和筛选后选择的功能，并具有流行的选择标准，例如AIC和（扩展）BIC。该软件包为用户提供了控制一系列筛选参数的灵活性，并可以容纳数值和分类功能输入。在广泛的数值示例中说明了包装的用法，其中可以很好地观察到SMLE的有希望的性能。
The sparsity-restricted maximum likelihood estimator (SMLE) has received considerable attention for feature screening in ultrahigh-dimensional regression. SMLE is a computationally convenient method that naturally incorporates the joint effects among features in the screening process. We develop a publicly available R package SMLE, which provides a user-friendly environment to carry out SMLE in generalized linear models. In particular, the package includes functions to conduct SMLE-screening and post-screening selection using SMLE with popular selection criteria such as AIC and (extended) BIC. The package gives users the flexibility in controlling a series of screening parameters and accommodates both numerical and categorical feature input. The usage of the package is illustrated on extensive numerical examples, where the promising performance of SMLE is well observed.
