Evaluating the Statistical Significance of Biclusters
Abstract
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.
Cite
Text
Lee et al. "Evaluating the Statistical Significance of Biclusters." Neural Information Processing Systems, 2015.Markdown
[Lee et al. "Evaluating the Statistical Significance of Biclusters." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/lee2015neurips-evaluating/)BibTeX
@inproceedings{lee2015neurips-evaluating,
title = {{Evaluating the Statistical Significance of Biclusters}},
author = {Lee, Jason and Sun, Yuekai and Taylor, Jonathan E},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {1324-1332},
url = {https://mlanthology.org/neurips/2015/lee2015neurips-evaluating/}
}