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dc.titleNon-parametric inferences based on general unbalanced ranked-set samples
dc.contributor.authorChen, Z.
dc.identifier.citationChen, Z. (2001). Non-parametric inferences based on general unbalanced ranked-set samples. Journal of Nonparametric Statistics 13 (2) : 291-310. ScholarBank@NUS Repository.
dc.description.abstractA general unbalanced ranked-set sample consists of independent order statistics each of which is out of a subsample from a common population. Such data can arise from two situations: (a) a designed ranked-set sampling (RSS) and (b) certain experimental process, e.g., the r-out-of-k systems in life testing experiments. There is no well accepted approach available so far in the literature for the effective analysis of such data. In this article, we develop methods for making inferences on various features of the population such as quantile, distribution function and moments etc., based on data of the above nature. The asymptotic properties of the methods are well established. Some simulation results are also provided for the vindication of the methods.
dc.subjectDistribution function estimation
dc.subjectLife testing
dc.subjectQuantile estimation
dc.subjectRanked-set sampling
dc.subjectStatistical functional
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleJournal of Nonparametric Statistics
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