ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2025
A statistical view of column subset selection.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Quantifying Anterior Cruciate Ligament Injury Resilience: A Screening and Composite Score Framework.Orthopaedic journal of sports medicine · 2026Article
- Principal variables analysis for non-Gaussian data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Second-order group knockoffs with applications to genome-wide association studies.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
We consider the problem of selecting a small subset of representative variables from a large dataset. In the computer science literature, this dimensionality reduction problem is typically formalized as column subset selection (CSS). Meanwhile, the typical statistical formalization is to find an information-maximizing set of principal variables. This paper shows that these two approaches are equivalent, and moreover, both can be viewed as maximum-likelihood estimation within a certain semi-parametric model. Within this model, we establish suitable conditions under which the CSS estimate is consistent in high dimensions, specifically in the proportional asymptotic regime where the number of variables over the sample size converges to a constant. Using these connections, we show how to efficiently (1) perform CSS using only summary statistics from the original dataset; (2) perform CSS in the presence of missing and/or censored data; and (3) select the subset size for CSS in a hypothesis testing framework.
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