This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 599
Type: Invited
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306187
Title: Inference with Transposable Data: Modeling the Effects of Row and Column Correlations
Author(s): Genevera I. Allen*+ and Rob Tibshirani
Companies: Stanford University and Stanford University
Address: 390 Serra Mall, Stanford, CA, 94305,
Keywords: microarrays ; multiple testing ; false discovery rate ; matrix-variate normal ; large-scale inference ; covariance estimation

We consider the problem of large-scale inference on row or column variables of matrix-data. Often this data is transposable, meaning that both the row and column variables are of potential interest. An example of this scenario is detecting significant genes in microarrays when the arrays may be dependent. By modeling covariances with the matrix-variate normal distribution, we give theoretical results and simulation studies revealing the problems with using common test-statistics, null distributions, and multiple testing procedures on highly correlated data. We solve these problems by estimating the row and column covariances simultaneously, with transposable regularized covariance models, and de-correlating or sphering the data as a pre-processing step. Results indicate that our method leads to 1) increases statistical power and 2) correct estimation of the false discovery rate.

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