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New Modifications Of Ranked Set Sampling For Estimating Some Population Parameters

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dc.contributor.authorAldrabseh, Mahmoud Zuhier Abdulraheem
dc.date.accessioned2026-05-04T03:50:14Z
dc.date.available2026-05-04T03:50:14Z
dc.date.issued2025-03
dc.description.abstractTraditional sampling methods often lack efficiency and incur high measurement costs, creating a need for more efficient approaches that minimize the number of units measured while ensuring accuracy. Ranked set sampling (rss) is a cost-effective sampling technique that enhances parameter estimation by combining simple random sampling (srs) with the judgmental ranking of sample sets before measurement. Although rss and its variations have shown promise in cost reduction, the impact of excluding extreme ranks on rss efficiency remains unexplored. This study introduces a novel sampling design, except extreme ranked set sampling (eerss), to improve the efficiency of key estimators. The proposed eerss design is evaluated and compared with existing sampling methods for estimating the population mean, variance, and strength-stress reliability
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24072
dc.language.isoen
dc.subjectEstimation theory
dc.titleNew Modifications Of Ranked Set Sampling For Estimating Some Population Parameters
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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