Activity Number:
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164
- Random and Mixed Effect Models
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #323908
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View Presentation
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Title:
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A Modified Mixed Model Approach to the Large Scale Multiple Testing Problem
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Author(s):
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John Grego* and Paramita Chakraborty and Chong Ma and James Lynch
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Companies:
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Univ of South Carolina and Department of Statistics, University of South Carolina and University of South Carolina Columbia and Department of Statistics, University of South Carolina
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Keywords:
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empirical Bayes ;
exponential tilting
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Abstract:
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In big data situations, such as microarray analysis, one source of lack of reproducibility is the number of false positives/discoveries that occur. One method uses a mixture contamination model to identify real significant (contaminant) cases based on the false discovery rate for each case. We propose a modification in which the model and screening criteria are adjusted by conditioning test results based on a general class of statistics. We use cross-validation to estimate the mixture distribution with one portion of the data and screen cases with the remaining portion. The cross-validation process allows us to explore biological networks for significant genes.
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Authors who are presenting talks have a * after their name.