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EnglishRanked set sampling is a statistical technique usually used for a variable of interest that may be difficult or expensive to measure, but whose units are simple to rank according to a cheap sorting criterion. In this paper, we revisit the Rao regression-type estimator in the context of the ranked set sampling. The expression of the minimum mean squared error is given and a comparative study, based on simulated and real data, is carried out to clearly show that the considered estimator outperforms some competitive estimators discussed in the recent literature.
| Titolo: | Improving mean estimation in ranked set sampling using the Rao regression-type estimator |
| Autori: | |
| Data di pubblicazione: | 2018 |
| Data ahead of print: | 8-giu-2018 |
| Stato di pubblicazione: | Pubblicato |
| Rivista: | |
| Abstract: | Ranked set sampling is a statistical technique usually used for a variable of interest that may be difficult or expensive to measure, but whose units are simple to rank according to a cheap sorting criterion. In this paper, we revisit the Rao regression-type estimator in the context of the ranked set sampling. The expression of the minimum mean squared error is given and a comparative study, based on simulated and real data, is carried out to clearly show that the considered estimator outperforms some competitive estimators discussed in the recent literature. |
| Handle: | http://hdl.handle.net/11368/2927708 |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1214/17-BJPS350 |
| URL: | https://projecteuclid.org/euclid.bjps/1528444868 |
| Appare nelle tipologie: | 1.1 Articolo in Rivista |
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http://hdl.handle.net/11368/2927708
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