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Multi‐sample inference for simple‐tree alternatives with ranked‐set samples
Author(s) -
Ozturk Omer,
Wolfe Douglas A.,
Alexandridis Roxana
Publication year - 2004
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2004.00341.x
Subject(s) - mathematics , statistics , confidence interval , inference , nonparametric statistics , cdf based nonparametric confidence interval , sample (material) , confidence distribution , coverage probability , order statistic , computer science , artificial intelligence , chemistry , chromatography
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.