Timezone: »
Poster
Evaluating the statistical significance of biclusters
Jason D Lee · Yuekai Sun · Jonathan E Taylor
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)
More from the Same Authors
-
2014 Poster: Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices »
Austin Benson · Jason D Lee · Bartek Rajwa · David F Gleich -
2014 Spotlight: Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices »
Austin Benson · Jason D Lee · Bartek Rajwa · David F Gleich -
2014 Poster: Exact Post Model Selection Inference for Marginal Screening »
Jason D Lee · Jonathan E Taylor -
2013 Poster: On model selection consistency of penalized M-estimators: a geometric theory »
Jason D Lee · Yuekai Sun · Jonathan E Taylor -
2013 Poster: Using multiple samples to learn mixture models »
Jason D Lee · Ran Gilad-Bachrach · Rich Caruana -
2013 Spotlight: Using multiple samples to learn mixture models »
Jason D Lee · Ran Gilad-Bachrach · Rich Caruana -
2012 Poster: Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form »
Jason D Lee · Yuekai Sun · Michael Saunders -
2010 Poster: Practical Large-Scale Optimization for Max-norm Regularization »
Jason D Lee · Benjamin Recht · Russ Salakhutdinov · Nati Srebro · Joel A Tropp