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
Inferring skeletal production from time-averaged assemblages: skeletal loss pulls the timing of production pulses towards the modern period
TL;DR: This work evaluates the joint effects of temporally variable production and skeletal loss on postmortem age-frequency distributions to determine how to detect fluctuations in production over the recent past from AFDs and shows that, relative to the true timing of past production pulses, the modes of AFDs will be shifted to younger age cohorts, causing the true age of past pulses to be underestimated.
Posted Content
The knockoff filter for FDR control in group-sparse and multitask regression
Ran Dai,Rina Foygel Barber +1 more
TL;DR: The group knockoff filter is proposed, a method for false discovery rate control in a linear regression setting where the features are grouped, and a set of relevant groups which have a nonzero effect on the response are selected.
Journal ArticleDOI
The p‐filter: multilayer false discovery rate control for grouped hypotheses
TL;DR: In this paper, the p-filter is proposed for controlling the false discovery rate (FDR) in a multi-layer FDR control problem with structural, spatial or temporal relatedness of the hypotheses.
Journal ArticleDOI
Between hard and soft thresholding: optimal iterative thresholding algorithms
Haoyang Liu,Rina Foygel Barber +1 more
TL;DR: This work examines the choice of the thresholding operator, and finds that commonly used thresholding operators, such as hard thresholding and soft thresholding, are suboptimal in terms of worst-case convergence guarantees.
Posted Content
The Function-on-Scalar LASSO with Applications to Longitudinal GWAS
TL;DR: Using the Framingham Heart Study, it is demonstrated how the LASSO tools can be used in genome-wide association studies, finding a number of genetic mutations which affect blood pressure and are therefore important for cardiovascular health.