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Our upcoming event for the Statistics Department Colloquium Series is scheduled for this Monday, April 24th from 12:00 – 1:00pm (ET) and will be an in-person presentation Science Center Rm. 316. The speaker will be Will Fithian who is an associate professor of statistics at UC Berkeley.
Title: Estimating the false discovery rate of model selection
Abstract: I will introduce a novel method for assessing the false discovery rate (FDR) of a model selection method such as the LASSO, forward-stepwise regression, or the graphical lasso. Our method gives an FDR estimate whose bias is provably non-negative, and can be employed alongside methods like cross-validation or SURE to trade off model selection accuracy against prediction error, or simply to warn the analyst about the likelihood that an estimator's FDR is large. I will also present the main technical tool for this method, conditional calibration, which can also be employed to adjust the Benjamini-Hochberg procedure for dependence, or to improve the power of knockoff methods.