Inference on Downton’s Bivariate Exponential Distribution Based on Moving Extreme Ranked Set Sampling

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

Estimation of a parameter of Morgenstern type bivariate exponential distribution by ranked set sampling

Ranked set sampling is applicable whenever ranking of a set of sampling units can be done easily by a judgement method or based on the measurement of an auxiliary variable on the units selected. In this work, we consider ranked set sampling, in which ranking of units are done based on measurements made on an easily and exactly measurable auxiliary variable X which is correlated with the study v...

Estimation of Variance of Normal Distribution using Ranked Set Sampling

Introduction     In some biological, environmental or ecological studies, there are situations in which obtaining exact measurements of sample units are much harder than ranking them in a set of small size without referring to their precise values. In these situations, ranked set sampling (RSS), proposed by McIntyre (1952), can be regarded as an alternative to the usual simple random sampling ...

Hypothesis Testing on the Parameters of Exponential, Pareto and Uniform Distributions Based on Extreme Ranked Set Sampling

The problem of hypothesis testing of the location parameter of the two-parameter exponential distribution and scale parameter of the pareto and uniform distribution are considered. We construct the test statistics based on Ranked Set Sampling (RSS) and Extreme RSS (ERSS) and compared their powers to the power of Uniformly Most Powerful (UMP) test by numerical computation. The results show that ...

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...