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Evaluating the statistical significance of biclusters
Jason D Lee · Yuekai Sun · Jonathan E Taylor

Tue Dec 08 04:00 PM -- 08:59 PM (PST) @ 210 C #68 #None

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.

Author Information

Jason D Lee (Stanford)
Yuekai Sun (Stanford University)
Jonathan E Taylor (Stanford University)

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