Regression Estimators in Ranked Set, Median Ranked Set and Neoteric Ranked Set Sampling

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 ...

Extreme ranked set sampling: A comparison with regression and ranked set sampling estimators

Ranked set sampling (RSS) assumed perfect ranking i.e. there will be no errors in ranking the units with respect to the variable of interest. In fact for most practical applications, it is not easy to rank the units without errors in ranking. There will be a loss in efficiency, i.e. RSS will give a larger variance due to the errors in ranking the units. To reduce the errors in ranking in estima...

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...

Ranked set sampling

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...