Ranked set sampling is a statistical technique usually used when measuring the variable of interest may be difficult or expensive, but it can be simple to rank the units 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.
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Titolo: | Improving mean estimation in ranked set sampling using the Rao regression-type estimator |
Autori: | |
Data di pubblicazione: | 2018 |
Rivista: | |
Handle: | http://hdl.handle.net/20.500.11770/132881 |
Appare nelle tipologie: | 1.1 Articolo in rivista |