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Activity Number:
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499
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2007 : 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 - #309116 |
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Title:
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Estimating the Distribution Function Using k-tuple Ranked Set Samples
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Author(s):
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Kaushik Ghosh*+ and Ram C. Tiwari
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Companies:
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New Jersey Institute of Technology and National Cancer Institute
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Address:
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University Heights, Newark, NJ, 07102,
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Keywords:
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Extreme ranked set sample ; Imperfect ranking ; Cost of sampling ; Empirical process
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Abstract:
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The basic assumption underlying the concept of ranked set sampling is that actual measurement of units is expensive whereas ranking is cheap. This may not be true in reality in certain cases where ranking may be moderately expensive. In such situations, based on total cost considerations, k-tuple ranked set sampling is known to be a viable alternative, where one selects k units (instead of one) from each ranked set. In this article, we consider estimation of the distribution function based on k-tuple ranked set samples when the cost of selecting and ranking units is not ignorable. We investigate estimation both in the balanced and unbalanced data case. Properties of the estimation procedure in the presence of ranking error are also investigated. Results of simulation studies as well as an application to a real data set are presented to illustrate some of the theoretical findings.
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