Estimating the false discovery rate of variable selection

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 (non-negative) 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. This is joint work with Yixiang Luo and Lihua Lei.

BIO 

Will Fithian is an associate professor of Statistics at UC Berkeley. He received his PhD in Statistics at Stanford in 2015, advised by Trevor Hastie.