This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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152
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Type:
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Invited
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Date/Time:
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Monday, August 2, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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JASA, Theory and Methods
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Abstract - #306255 |
Title:
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Correlated z-Values and the Accuracy of Large-Scale Statistical Estimates
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Author(s):
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Bradley Efron*+
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Companies:
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Stanford University
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Address:
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Dept of Statistics, Bloomington, IN, 94305, USA
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Keywords:
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rms correlation ;
non-null z-values ;
correlation penalty ;
Mehler's identity ;
empirical process ;
acceleration
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Abstract:
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We consider large-scale studies in which there are hundreds or thousands of correlated cases to investigate, each represented by its own normal variate, typically a z-value. A familiar example is provided by a microarray experiment comparing healthy with sick subjects' expression levels for thousands of genes. This paper concerns the accuracy of summary statistics for the collection of normal variates, such as their empirical cdf or a false discovery rate statistic. It seems like we must estimate an N-by-N correlation matrix, N the number of cases, but our main result shows that this is not necessary: good accuracy approximations can be based on the root mean square correlation over all N(N-1)/2 pairs, a quantity often easily estimated. A second result shows that z-values closely follow normal distributions even under non-null conditions, supporting application of the main theorem.
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Authors who are presenting talks have a * after their name.
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