论文标题
在高维变量选择器之间利用分歧以实现不确定性可视化
Exploiting disagreement between high-dimensional variable selectors for uncertainty visualization
论文作者
论文摘要
我们提出了组合的选择和不确定性可视化器（CSUV），它在高维线性回归中估算了真实协变量的集合，并通过在不同的基本选择器之间利用（DIS）一致性来可视化选择不确定性。我们提出的方法选择了通过在亚采样数据上的不同变量选择方法最常选择的协变量。该方法是通用的，可以与不同的现有变量选择方法一起使用。我们使用真实和模拟数据演示其可变选择性能。可变选择方法及其不确定性插图工具可公开使用，以r软件包CSUV（https://github.com/christineyuen/csuv）。该图形工具也可以通过https://csuv.shinyapps.io/csuv在线获得
We propose Combined Selection and Uncertainty Visualizer (CSUV), which estimates the set of true covariates in high-dimensional linear regression and visualizes selection uncertainties by exploiting the (dis)agreement among different base selectors. Our proposed method selects covariates that get selected the most frequently by the different variable selection methods on subsampled data. The method is generic and can be used with different existing variable selection methods. We demonstrate its variable selection performance using real and simulated data. The variable selection method and its uncertainty illustration tool are publicly available as R package CSUV (https://github.com/christineyuen/CSUV). The graphical tool is also available online via https://csuv.shinyapps.io/csuv
