JSM 2011 Online Program

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Abstract Details

Activity Number: 617
Type: Contributed
Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300952
Title: Correlation and Variance Stabilization in Large-Scale Experiments Comparing Two Groups
Author(s): Dilan Paranagama*+ and Gary L. Gadbury
Companies: Kansas State University and Kansas State University
Address: Dept. of Statistics , Manhattan, KS, 66506,
Keywords: FDR ; dependence structure ; high dimension ; conditional ; network
Abstract:

Multiple testing research has undergone renewed focus in recent years as advances in high throughput technologies have produced data on unprecedented scales. Much of the focus has been on false discovery rates and related quantities that are estimated (or controlled for) in large scale multiple testing situations. Some estimators may have high variance in the presence of correlation, and the effect of this variance on interpretations of estimators has received less attention in the literature. Recent papers by Efron have directly addressed this issue. This talk begins by demonstrating the effect of dependence structure on the variance of the number of discoveries and the false discovery proportion (FDP). A variance of the number of discoveries is shown and the density of a test statistic, conditioned on the status (reject or failure to reject) of a different correlated test, is derived.


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