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Tuesday, December 8 • 19:00 - 23:59
Evaluating the statistical significance of biclusters

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Biclustering (also known as submatrix localization) is a problem of high practical relevance in exploratory analysis of high-dimensional data. We develop a framework for performing statistical inference on biclusters found by score-based algorithms. Since the bicluster was selected in a data dependent manner by a biclustering or localization algorithm, this is a form of selective inference. Our framework gives exact (non-asymptotic) confidence intervals and p-values for the significance of the selected biclusters. Further, we generalize our approach to obtain exact inference for Gaussian statistics.


Tuesday December 8, 2015 19:00 - 23:59
210 C #68

Attendees (2)