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.

Improving mean estimation in ranked set sampling using the Rao regression-type estimator / Pelle, Elvira; Perri, Pier Francesco. - In: REVISTA BRASILEIRA DE PROBABILIDADE E ESTATÍSTICA. - ISSN 0103-0752. - 32:3(2018), pp. 467-496. [10.1214/17-BJPS350]

Improving mean estimation in ranked set sampling using the Rao regression-type estimator

Pelle, Elvira;
2018

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.
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Improving mean estimation in ranked set sampling using the Rao regression-type estimator / Pelle, Elvira; Perri, Pier Francesco. - In: REVISTA BRASILEIRA DE PROBABILIDADE E ESTATÍSTICA. - ISSN 0103-0752. - 32:3(2018), pp. 467-496. [10.1214/17-BJPS350]
Pelle, Elvira; Perri, Pier Francesco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1171629
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