论文标题
具有二元结果的高维数据的迭代变量选择
Iterative variable selection for high-dimensional data with binary outcomes
论文作者
论文摘要
我们为具有二进制结果的高维数据提出了一种迭代变量选择方案。该方案采用结构化的屏幕和选择框架，并在同一中使用非本地先验的贝叶斯模型选择。结构化筛选是基于独立变量与结果的关联，该结果是根据最大边缘可能性估计量来衡量的。绩效与几种众所周知的方法相比，就真实的正率和错误的发现率而言，我们提出的方法是稀疏高维变量选择和二进制结果的竞争替代方法。该方法已在R软件包Gwasinlps中实现。
We propose an iterative variable selection scheme for high-dimensional data with binary outcomes. The scheme adopts a structured screen-and-select framework and uses non-local prior-based Bayesian model selection within the same. The structured screening is based on the association of the independent variables with the outcome which is measured in terms of the maximum marginal likelihood estimator. Performance comparison with several well-known methods in terms of true positive rate and false discovery rate shows that our proposed method stands to be a competitive alternative for sparse high-dimensional variable selection with binary outcomes. The method has been implemented within the R package GWASinlps.
