Abstract Details
Activity Number:
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613
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Type:
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Contributed
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Date/Time:
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Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #316234
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Title:
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Sampling for Conditional Inference on Contingency Tables
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Author(s):
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Robert Eisinger*
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Companies:
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Keywords:
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Abstract:
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We propose new sequential importance sampling methods for sampling contingency tables with given margins. The proposal for each method is based on asymptotic approximations to the number of tables with fixed margins. These methods generate tables that are very close to the uniform distribution. The tables, along with their importance weights, can be used to approximate the null distribution of test statistics and calculate the total number of tables. We study the asymptotic behavior of the algorithm and show that the importance weight is bounded as the table size and sum grow to infinity. This result implies that the method works well even for large sparse tables. We apply the methods to a number of examples and demonstrate an improvement over other methods in a variety of real problems.
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Authors who are presenting talks have a * after their name.
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