Abstract Details
Activity Number:
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470
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Defense and National Security
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Abstract #311984
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Title:
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A Skewed Version of the Robbins-Monro-Joseph Procedure for Binary Response
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Author(s):
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Dianpeng Wang*+ and Yubin Tian and C. F. Jeff Wu
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Companies:
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Beijing Institute of Technology and Beijing Institute of Technology and Georgia Institute of Technology
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Keywords:
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Robbins-Monro stochastic approximation ;
Asymmetric loss function ;
Extreme quantiles
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Abstract:
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The Robbins-Monro stochastic approximation has been used in sensitivity testing experiments. Joseph (2004) recognized that the procedure is not well suited for binary data and proposed a modification which gives better performance for p between 0.1 and 0.9. However, for extreme p values, say p>0.01 or p< 0.99, which is used for high precision requirement, this modification does not perform well. Here we propose a skewed version of the Robbins-Monro-Joseph procedure based on an asymmetric loss function. It can speed up convergence by employing different penalties for under-shooting and over-shooting to reduce the expected loss. Simulation studies show that this new procedure performs substantially better for extreme quantiles.
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Authors who are presenting talks have a * after their name.
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