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Exploiting disagreement between high-dimensional variable selectors for uncertainty visualization

Yuen, Christine and Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X (2021) Exploiting disagreement between high-dimensional variable selectors for uncertainty visualization. Journal of Computational and Graphical Statistics. ISSN 1061-8600 (In Press)

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Identification Number: 10.1080/10618600.2021.2000421

Abstract

We propose Combined Selection and Uncertainty Visualizer (CSUV), which visualises selection uncertainties for covariates in high-dimensional linear regression by exploiting the (dis)agreement among different base selectors. Our proposed method highlights covariates that get selected the most frequently by the different base variable selection methods on subsampled data. The method is generic and can be used with different existing variable selection methods. We demonstrate its performance using real and simulated data. The corresponding R package CSUV is at https://github.com/christineyuen/CSUV, and the graphical tool is also available online via https://csuv.shinyapps.io/csuv.

Item Type: Article
Official URL: https://www.tandfonline.com/toc/ucgs20/current
Additional Information: © 2021 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 22 Oct 2021 09:12
Last Modified: 26 Nov 2021 11:48
URI: http://eprints.lse.ac.uk/id/eprint/112480

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