dc.contributor.author Albatineh, Ahmad dc.contributor.author Kibria, Golam dc.contributor.author Zogheib, Bashar dc.date.accessioned 2016-04-07T08:38:21Z dc.date.available 2016-04-07T08:38:21Z dc.date.issued 2014 dc.identifier.uri http://hdl.handle.net/11675/837 dc.description.abstract Sharma and Krishna (1994) derived mathematically an appealing asymptotic confidence interval for the population signal-to-noise ratio (SNR). In this paper, an evaluation of the performance of this interval using monte carlo simulations using randomly generated data from normal, log-normal, $\chi^2$, Gamma, and Weibull distributions three of which are discussed in Sharma and Krishna (1994). Simulations revealed that its performance, as measured by coverage probability, is totally dependent on the amount of noise introduced. A proposal for using ranked set sampling (RSS) instead of simple random sampling (SRS) improved its performance. It is recommended against using this confidence interval for data from a log-normal distribution. Moreover, this interval performs poorly in all other distributions unless the SNR is around one. dc.relation.journal International Journal of Advanced Statistics and Probability dc.title Asymptotic Sampling Distribution of Inverse Coefficient of Variation and its Applications: Revisited dc.type Journal Article dc.type Peer-Reviewed dc.journal.volume 2 dc.journal.issue 1 dc.article.pages 15-20 dc.identifier.doi DOI: 10.14419/ijasp.v2i1.1475 dc.identifier.url https://sciencepubco.com/index.php/IJASP/article/view/1475
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