Estimation of the means of the bivariate normal using moving extreme ranked set sampling with concomitant variable
The estimation of the means of the bivariate normal distribution, based on a sample obtained using a modification of the moving extreme ranked set sampling technique (MERSS) is considered. The modification involves using a concomitant random variable. Nonparametric-type methods as well as the maximu...
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| Veröffentlicht in: | Statistical papers (Berlin, Germany) Germany), 2007-04, Vol.48 (2), p.179-195 |
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| container_title | Statistical papers (Berlin, Germany) |
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| creator | Al-Saleh, Mohammad Fraiwan Al-Ananbeh, Ahmad Mohammad |
| description | The estimation of the means of the bivariate normal distribution, based on a sample obtained using a modification of the moving extreme ranked set sampling technique (MERSS) is considered. The modification involves using a concomitant random variable. Nonparametric-type methods as well as the maximum likelihood estimation are considered. The estimators obtained are compared to their counterparts based on simple random sampling (SRS). It appears that the suggested estimators are more efficient. Also, MERSS with concomitant variable is easier to use in practice than the usual ranked set sampling (RSS) with concomitant variable. The issue of robustness of the procedure is addressed. Real trees data set is used for illustration. [PUBLICATION ABSTRACT] |
| doi_str_mv | 10.1007/s00362-006-0325-8 |
| format | Article |
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| ispartof | Statistical papers (Berlin, Germany), 2007-04, Vol.48 (2), p.179-195 |
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| language | eng |
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| source | Springer Nature - Complete Springer Journals; EBSCOhost Business Source Complete |
| subjects | Estimating techniques Normal distribution Random variables Sample size Sampling techniques Statistics Studies |
| title | Estimation of the means of the bivariate normal using moving extreme ranked set sampling with concomitant variable |
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