Advances and Applications in Statistics
Volume 49, Issue 2, Pages 105 - 116
(August 2016) http://dx.doi.org/10.17654/AS049020105 |
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RATIO ESTIMATOR IN ADAPTIVE CLUSTER SAMPLING BASED ON RANKED SET
Nipaporn Chutiman, Monchaya Chiangpradit and Sujitta Suraphee
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Abstract: Ranked set sampling (RSS), as suggested by McIntyre [1], is a useful method for improving estimates of mean and variance. Samawi and Muttlak [2] developed the ratio estimator in RSS and showed that this ratio estimator is more efficient than ratio estimator in simple random sampling (SRS) method. Adaptive cluster sampling, as suggested by Thompson [3], is a useful method for rare population such as rare species in animal or plant population. In adaptive cluster sampling, an initial sample of units is selected by SRS method. In this paper, we use the advantage of both schemes, that is, sampling design of the adaptive cluster sampling and RSS method for selection of the initial sample. The estimator in this study was of the ratio estimator type. The numerical results showed that the ratio estimator in adaptive cluster sampling based on ranked set sampling is more efficient than the ratio estimators in ranked set sampling. |
Keywords and phrases: adaptive cluster sampling, ranked set, ratio estimator. |
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