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Abstract Details
Activity Number:
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467
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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Abstract - #300527 |
Title:
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A New Approach to Estimate the Critical Constant of Selection Procedures
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Author(s):
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Min Li*+ and Jack E. Chen
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Companies:
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California State University at Sacramento and BASF Corporation
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Address:
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6000 J STREET, SACRAMENTO, CA, 95819-6088,
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Keywords:
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ranking and selection ;
sample size allocation ;
simulation
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Abstract:
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A solution to the ranking and selection problem of determining a subset of size m containing at least c of the v best from k normal distribution has been developed. The best distributions are those having, for example, (i) the smallest means, or (ii) the smallest variances. This paper reviews various applicable algorithms and supplies the operating constants needed to apply these solutions. The constants are computed using a histogram approximation algorithm and Monte Carlo integration.
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