论文标题
确保对跨性图形模型进行筛选
Sure Screening for Transelliptical Graphical Models
论文作者
论文摘要
我们提出了一种肯定的筛选方法，用于在高维度设置中恢复跨性图形模型的结构。我们通过阈值使用Kendall的TAU统计量获得样品相关矩阵的估计值来估计部分相关图。在对相关图和部分相关图之间关系的简单假设下，我们表明，概率很高，估计的边缘集包含真实的边缘集，并且控制了估计的边缘集的大小。我们开发一个阈值值，可以控制预期的误报率。在仿真和股票数据集中，我们表明，跨性图形筛选筛选具有相当有竞争力的竞争性，以进行图形估计的更多计算要求。
We propose a sure screening approach for recovering the structure of a transelliptical graphical model in the high dimensional setting. We estimate the partial correlation graph by thresholding the elements of an estimator of the sample correlation matrix obtained using Kendall's tau statistic. Under a simple assumption on the relationship between the correlation and partial correlation graphs, we show that with high probability, the estimated edge set contains the true edge set, and the size of the estimated edge set is controlled. We develop a threshold value that allows for control of the expected false positive rate. In simulation and on an equities data set, we show that transelliptical graphical sure screening performs quite competitively with more computationally demanding techniques for graph estimation.
