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Activity Number:
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369
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301438 |
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Title:
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Ordered Inference Using Observed Confidence Levels
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Author(s):
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Alan M. Polansky*+
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Companies:
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Northern Illinois University
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Address:
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Division of Statistics, De Kalb, IL, 60115,
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Keywords:
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Bootstrap ; Edgeworth Expansion ; Normal Model ; Restricted Inference ; Multiple Comparisons ; Error Rate
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Abstract:
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Statistical inference on the ordering of the elements of a mean vector is an important issue in many applied problems. Many statistical tests of hypotheses to detect these orderings have been developed both within the normal model, and outside the normal model using nonparametric methods. Estimates as well as confidence regions have also been developed for the mean vector under constraints imposed by these ordering models. In order to attempt to distinguish between ordered models, multiple testing procedures are often used to control the overall error rate of the sequence of tests. This paper shows how observed confidence levels allow for the exploration of very general models for the ordering of the elements of a mean vector without the need for specialized asymptotic theory or multiple testing methods. The methods are applied to several well-known examples.
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