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.
翻译:我们为具有二元结果的高维数据提出了一个迭代变量选择方案。这个方案采用了一个结构化的筛选和选择框架,并在同一框架内使用非本地先前基于巴耶斯模式的选择。结构化筛选的基础是将独立变量与以最大边际概率估计值衡量的结果联系起来。在真实正率和虚假发现率方面,与若干众所周知的方法进行绩效比较表明,我们所提议的方法对于带有二元结果的稀疏高维变量选择而言,是一种竞争性的替代方法。该方法已在R包 GWASinlps 中实施。