Estimation of the Population Mean Using Paired Ranked Set Sampling

Abstract

In the situation where the sampling units in a study can be easily ranked than quantified, the ranked set sampling methods are found to be more efficient and cost effective as compared to SRS. In this paper we propose an estimator of the population mean using paired ranked set sampling (RSS) method. The proposed estimator is an unbiased estimator of the population mean when the set size is even. In case of odd set size the estimator is unbiased when the underlying distribution is symmetric. It is shown that the proposed estimator is more efficient than its counterpart SRS method for all distributions considered in this study.

On the improvement of paired ranked set sampling to estimate population mean

“…Muttlak (2003) suggested quartile RSS (QRSS) to get more representative units as a sample. Biradar and Santosha (2015) proposed Independent Extreme RSS (IERSS) by modifying original RSS. It is well understood that population parameters like mean and variance might not be estimated more efficiently when the population under study is suspected of containing some extreme values or outliers.…”

On the population median estimation using quartile double ranked set sampling

“…Entropy estimation and goodnessof-fit tests for the inverse Gaussian and Laplace distributions using paired ranked set sampling method is suggested by Al-Omari and Haq (2015). Biradar and Santosha (2015) proposed estimation of the population mean using paired ranked set sampling. Santos and Barrios (2015) considered predictive accuracy of logistic regression model using ranked set samples.…”