Abstract Details
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Activity Number:
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413
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #307906 |
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Title:
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Bayesian Large-Scale Multiple Testing for Time Series Data
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Author(s):
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Xia Wang and Ali Shojaie*+ and Jian Zou
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Companies:
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University of Cincinnati and University of Washington and Indiana University-Purdue University Indianapolis
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Keywords:
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Bayesian Method ;
False Discovery Rate ;
Hidden Markov Chain ;
Multiple Comparisons
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Abstract:
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In this project, we consider the problem of massive data multiple testing under temporal dependence. The observed data is assumed to be generated from an underlying two-state hidden Markov model. Bayesian methods are applied to develop the testing algorithm by optimizing the false negative rate while controlling the false discovery rate which is comparable to Sun and Cai (2009).
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Authors who are presenting talks have a * after their name.
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