Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105241
Title: Non-parametric inferences based on general unbalanced ranked-set samples
Authors: Chen, Z. 
Keywords: Distribution function estimation
Life testing
Quantile estimation
Ranked-set sampling
Statistical functional
Issue Date: 2001
Citation: Chen, Z. (2001). Non-parametric inferences based on general unbalanced ranked-set samples. Journal of Nonparametric Statistics 13 (2) : 291-310. ScholarBank@NUS Repository.
Abstract: A 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.
Source Title: Journal of Nonparametric Statistics
URI: http://scholarbank.nus.edu.sg/handle/10635/105241
ISSN: 10485252
Appears in Collections:Staff Publications

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