Abstract Details
Activity Number:
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229
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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International Indian Statistical Association
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Abstract #313362
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Title:
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Bootstrap Precedence Tests for K-Samples
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Author(s):
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Rajarshi Dey*+ and Paul Nelson
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Companies:
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and Kansas State University
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Keywords:
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Precedence probability ;
K-sample Test ;
Bootstrap ;
Effect Size ;
Location, Scale and Shape problem
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Abstract:
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Let X_{i} be a random variable with continuous distribution function F_{i}; i=1,2,.,K. Also, let Q be the set of all permutations of the numbers (1,2,.,K). Then, P(X_{i1}< X_{i2} < .< X_{ik}) is a precedence probability if (i1, i2, ., ik) belongs to Q. A new approximate nonparametric test for the equality of K> 2 continuous distributions based on independent random samples is developed and explored. The test uses a one sided confidence interval for the maximum of all K! precedence probabilities to construct the critical region. For K = 2 the test is comparable in structure to a two sided Mann-Whitney test. Simulations indicate that the new test effectively holds its nominal type I error rate and has competitive power compared to standard tests in a wide variety of settings, including differences in location, scale and shape.
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Authors who are presenting talks have a * after their name.
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