Multi-sample inference for simple-tree alternatives with ranked-set samples

Summary This paper develops a non‐parametric multi‐sample inference for simple‐tree alternatives for ranked‐set samples. The multi‐sample inference provides simultaneous one‐sample sign confidence intervals for the population medians. The decision rule compares these intervals to achieve the desired...

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Bibliographic Details
Published in:Australian & New Zealand journal of statistics 2004-09, Vol.46 (3), p.443-455
Main Authors: Ozturk, Omer, Wolfe, Douglas A., Alexandridis, Roxana
Format: Article
Language:English
Subjects:
Design
Information
Mann-Whitney-Wilcoxon test
Mathematical models
median test
mode
mood test
non-parametric testing
Pitman efficiency
Statistical methods
unequal allocation
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Summary:Summary This paper develops a non‐parametric multi‐sample inference for simple‐tree alternatives for ranked‐set samples. The multi‐sample inference provides simultaneous one‐sample sign confidence intervals for the population medians. The decision rule compares these intervals to achieve the desired type I error. For the specified upper bounds on the experiment‐wise error rates, corresponding individual confidence coefficients are presented. It is shown that the testing procedure is distribution‐free. To achieve the desired confidence coefficients for multi‐sample inference, a nonparametric confidence interval is constructed by interpolating the adjacent order statistics. Interpolation coefficients and coverage probabilities are provided, along with the nominal levels.
ISSN:1369-1473
1467-842X
DOI:10.1111/j.1467-842X.2004.00341.x