Brazilian Journal of Probability and Statistics

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

Elvira Pelle and Pier Francesco Perri

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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.

Article information

Source
Braz. J. Probab. Stat., Volume 32, Number 3 (2018), 467-496.

Dates
Received: April 2016
Accepted: January 2017
First available in Project Euclid: 8 June 2018

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1528444868

Digital Object Identifier
doi:10.1214/17-BJPS350

Mathematical Reviews number (MathSciNet)
MR3812378

Zentralblatt MATH identifier
06930035

Keywords
Auxiliary variable order statistics product-type estimators ratio-type estimators bivariate Normal distribution simulation

Citation

Pelle, Elvira; Perri, Pier Francesco. Improving mean estimation in ranked set sampling using the Rao regression-type estimator. Braz. J. Probab. Stat. 32 (2018), no. 3, 467--496. doi:10.1214/17-BJPS350. https://projecteuclid.org/euclid.bjps/1528444868


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