Abstract Details
Activity Number:
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642
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Type:
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Contributed
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Date/Time:
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Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #308831 |
Title:
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The Projack: A Resampling Approach for Prediction of Ranked Effect Sizes and Estimation of Normal Means
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Author(s):
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Yihui Zhou*+ and Fred Wright
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Companies:
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University of North Carolina, Chapel Hill and The University of North Carolina
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Keywords:
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Abstract:
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The problem of ranked inference arises in a number of settings, for which the investigator wishes to perform parameter inference after ranking a set of m statistics. In contrast to inference for a single hypothesis, the ranking procedure introduces considerable bias. Even for independent z statistics, the problem is nontrivial, and has connections to the classic problem of estimating a multivariate mean vector. We introduce the projack, a resampling-based procedure that provides accurate estimates and coverage intervals of the underlying mean parameter for a set of possibly correlated z statistics. The approach is initially motivated for the setting where original data is available for resampling, a simple extension is applicable when only the vector of z values is available. We illustrate the projack for gene expression data, and for classical datasets used for shrinkage estimation and prediction.
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Authors who are presenting talks have a * after their name.
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