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August 2018 Improving mean estimation in ranked set sampling using the Rao regression-type estimator
Elvira Pelle, Pier Francesco Perri
Braz. J. Probab. Stat. 32(3): 467-496 (August 2018). DOI: 10.1214/17-BJPS350


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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Elvira Pelle. Pier Francesco Perri. "Improving mean estimation in ranked set sampling using the Rao regression-type estimator." Braz. J. Probab. Stat. 32 (3) 467 - 496, August 2018.


Received: 1 April 2016; Accepted: 1 January 2017; Published: August 2018
First available in Project Euclid: 8 June 2018

zbMATH: 06930035
MathSciNet: MR3812378
Digital Object Identifier: 10.1214/17-BJPS350

Keywords: auxiliary variable , Bivariate normal distribution , order statistics , product-type estimators , ratio-type estimators , simulation

Rights: Copyright © 2018 Brazilian Statistical Association


Vol.32 • No. 3 • August 2018
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