[Ml-stat-talks] Fwd: Statistics talk on Monday from James Scott (UT Austin)
bee at princeton.edu
Sun Sep 20 19:35:09 EDT 2015
Talk of interest on Monday.
Time: Monday, 9/21 @ 3pm
Location: PNI 130
Department of Statistics & Data Science and
Department of Information, Risk, and Operations Management
The University of Texas at Austin
*False discovery rate smoothing*
*Abstract:* We present false discovery rate smoothing, an empirical-Bayes
method for exploiting spatial structure in large multiple-testing problems.
FDR smoothing automatically finds spatially localized regions of
significant test statistics. It then relaxes the threshold of statistical
significance within these regions, and tightens it elsewhere, in a manner
that controls the overall false-discovery rate at a given level. This
results in increased power and cleaner spatial separation of signals from
noise. The approach requires solving a non-standard high-dimensional
optimization problem, for which an efficient augmented-Lagrangian algorithm
is presented. We demonstrate that FDR smoothing exhibits state-of-the-art
performance on simulated examples. We also apply the method to a data set
from an fMRI experiment on spatial working memory, where it detects
patterns that are much more biologically plausible than those detected by
existing FDR-controlling methods. All code for FDR smoothing is publicly
available in Python and R.
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