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Exploiting disagreement between high-dimensional variable selectors for uncertainty visualization
Christine Yuen, Piotr Fryzlewicz
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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
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