Author： Alexandridis Roxana
Publisher： Taylor & Francis Ltd
ISSN： 0361-0926
Source： Communications in Statistics: Theory and Methods, Vol.38, Iss.12, 2009-01, pp. : 2126-2143
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Abstract
This article develops robust statistical inference against imperfect ranking in a ranked set sample data obtained from a family of discrete distributions. We provide a class of disparity estimators that minimizes the disparities between the empirical and model distributions. It is shown that the Hellinger disparity estimator in this class is the most stable estimator in the neighborhood of an epsilon-contaminated judgment ranking model. When the true model is equal to assumed model and there is no ranking error, all disparity estimators are asymptotically efficient. A simulation study is provided to discuss the finite sample properties of the estimator. The proposed procedure is applied to a ranked set sample data in a water quality study.
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