## Brazilian Journal of Probability and Statistics

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

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

Elvira Pelle and Pier Francesco Perri

#### 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