Other approaches to bivariate ranked set sampling

Test of the Correlation Coefficient in Bivariate Normal Populations Using Ranked Set Sampling

Ranked Set Sampling (RSS) is a statistical method for data collection that leads to more efficient estimators than competitors based on Simple Random Sampling (SRS). We consider testing the correlation coefficient of bivariate normal distribution based on Bivariate RSS (BVRSS). Under one-sided and two-sided alternatives, we show that the new tests based on BVRSS are more powerful than the usua...

Estimation Using Bivariate Extreme Ranked Set Sampling With Application To The Bivariate Normal Distribution

Parametric Ranked Set Sampling*

Ranked set sampling was introduced by McIntyre (1952, Australian Journal of Agricultural Research, 3, 385-390) as a cost-effective method of selecting data if observations are much more cheaply ranked than measured. He proposed its use for estimating the population mean when the distribution of the data was unknown. In this paper, we examine the advantage, if any, that this method of sampling h...

Stratified Median Ranked Set Sampling: Optimum and Proportional Allocations

In this paper, for the Stratified Median Ranked Set Sampling (SMRSS), proposed by Ibrahim et al. (2010), we examine the proportional and optimum sample allocations that are two well-known methods for sample allocation in stratified sampling. We show that the variances of the mean estimators of a symmetric population in SMRSS using optimum and proportional allocations to strata are smaller than ...

Ranked Set Sampling

This paper is intended to provide the reader with an introduction to ranked set sampling, a statistical technique for data collection that generally leads to more efficient estimators than competitors based on simple random samples. Methods for obtaining ranked set samples are described and the structural differences between ranked set samples and simple random samples are discussed. Properties...