R
Rina Foygel Barber
Researcher at University of Chicago
Publications - 121
Citations - 3990
Rina Foygel Barber is an academic researcher from University of Chicago. The author has contributed to research in topics: False discovery rate & Inference. The author has an hindex of 28, co-authored 103 publications receiving 2699 citations.
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Journal ArticleDOI
Controlling the false discovery rate via knockoffs
TL;DR: In this article, the authors introduce the knockoff filter, a new variable selection procedure for controlling the false discovery rate (FDR) in the statistical linear model whenever there are at least as many observations as variables.
Journal ArticleDOI
Controlling the False Discovery Rate via Knockoffs
TL;DR: The knockoff filter is introduced, a new variable selection procedure controlling the FDR in the statistical linear model whenever there are at least as many observations as variables, and empirical results show that the resulting method has far more power than existing selection rules when the proportion of null variables is high.
Journal ArticleDOI
Predictive inference with the jackknife
TL;DR: In this article, the authors introduce the jackknife+ method for constructing predictive confidence intervals, which is based on the leave-one-out predictions at the test point to account for the variability in the fitted regression function Assuming exchangeable training samples, this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically.
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A knockoff filter for high-dimensional selective inference
TL;DR: It is proved that the high-dimensional knockoff procedure 'discovers' important variables as well as the directions (signs) of their effects, in such a way that the expected proportion of wrongly chosen signs is below the user-specified level.
Posted Content
Conformal Prediction Under Covariate Shift
TL;DR: It is shown that a weighted version of conformal prediction can be used to compute distribution-free prediction intervals for problems in which the test and training covariate distributions differ, but the likelihood ratio between these two distributions is known.