Estimating the false discovery rate of variable selection
Abstract
We introduce a generic estimator for the false discovery rate of any model selection procedure, in common statistical modeling settings including the Gaussian linear model, Gaussian graphical model, and model-X setting. We prove that our method has a conservative (nonnegative) bias in finite samples under standard statistical assumptions, and provide a bootstrap method for assessing its standard error. For methods like the Lasso, forward-stepwise regression, and the graphical Lasso, our estimator serves as a valuable companion to cross-validation, illuminating the tradeoff between prediction error and variable selection accuracy as a function of the model complexity parameter.
Fields of Study & Subjects
Genetic and phenotypic traits in livestock
Publication details
- Journal / source
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Volume
- 54
- Issue
- 4
- Pages
- 1793–1818
- Publication date
- 1 August 2026
- Language
- EN