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.

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

PERRI, PIER FRANCESCO
2018

Abstract

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.
auxiliary variable, order statistics, product-type estimators, ratio-type estimators, bivariate Normal distribution, simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/132881
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