Abstract Details
Activity Number:
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553
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Type:
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Contributed
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Date/Time:
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Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #315495
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Title:
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Statistical Characteristics of Coverage Optimization Based on a Sample Mean Approach
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Author(s):
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Martin Levy* and James J. Cochran and Zhiyuan Dong
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Companies:
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University of Cincinnati and The University of Alabama and Integral Analytics
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Keywords:
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optimization ;
coverage probability ;
multinomial approximation ;
asymptotic distribution ;
weak consititency ;
asymptoticly unbiased estimator
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Abstract:
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We develop a sample mean approach for estimating the optimal coverage in a class of binary integer linear programs called the maximal coverage problem (MCP) over a very large population. Specifically, we propose randomly (or otherwise) splitting the population into t subpopulations and solving the MCP over these computationally manageable t subpopulations. We demonstrate the reduction in estimation bias for the maximand (minimand) that results from this approach, and we provide some asymptotic results. Specifically, we establish weak convergence and consistency results for this problem and show that the mean approximation approach produces an asymptotically unbiased estimation result (in contrast to other approaches). We report the results of the application of our approach on an instance of the magazine subscription problem.
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Authors who are presenting talks have a * after their name.
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