Improved Estimation from Ranked Set Sampling (original) (raw)
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Estimation Of Population Mean Using Ranked Set Sampling
In the present study, a modified ratio cum product type estimate for estimating the population mean in rank set sampling has been suggested. The bias and MSE (mean squared error) of all the discussed estimators are obtained. Comparison of the proposed estimator with the Samawi and Muttlak (1996) estimator is carried out. By comparison, the condition which make the proposed more efficient than the other estimators are found.
Estimation of the Population Mean Using Paired Ranked Set Sampling
Open Journal of Statistics, 2015
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
In ecological and environmental sampling the quantification of units is either difficult or overly demanding in terms of the time, money, workload, it requires. For this reason efficient and cost-effective sampling methods need to be devised for data collecting. The most commonly used method for this purpose is the Ranked Set Sampling (RSS). In this paper, a sampling scheme called Improved Paired Ranked Set Sampling (IPRSS) is proposed to estimate the population mean. The performance of the proposed IPRSS is evaluated under perfect and imperfect rankings. A simulation study based on selected hypothetical distributions and a real-life data set showed that IPRSS is more precise than RSS, Paired RSS (PRSS) or Extreme RSS (ERSS).
www.ijstr.org Improvement Over General And Wider Class of Estimators Using Ranked Set Sampling
2015
Abstract: In this paper, Improvement over general and wider class of estimators of finite population means using ranked set sampling is investigated. Ranked set sampling (RSS) was first suggested to increase the efficiency of estimator of the population mean. The first order approximation to the bias and mean square error (MSE) of the investigated estimators are obtained. Theoretically, it is shown that these suggested estimators are more efficient than the general and wider class of estimators in simple random sampling.
Improvement Over General And Wider Class of Estimators Using Ranked Set Sampling
International Journal of Scientific & Technology Research, 2012
Abstract: In this paper, Improvement over general and wider class of estimators of finite population means using ranked set sampling is investigated. Ranked set sampling (RSS) was first suggested to increase the efficiency of estimator of the population mean. The first order approximation to the bias and mean square error (MSE) of the investigated estimators are obtained. Theoretically, it is shown that these suggested estimators are more efficient than the general and wider class of estimators in simple random sampling.
A cost analysis of ranked set sampling to estimate a population mean
Environmetrics, 2005
Ranked set sampling (RSS) can be a useful environmental sampling method when measurement costs are high but ranking costs are low. RSS estimates of the population mean can have higher precision than estimates from a simple random sample (SRS) of the same size, leading to potentially lower sampling costs from RSS than from SRS for a given precision. However, RSS introduces ranking costs not present in SRS; these costs must be considered in determining whether RSS is cost effective. We use a simple cost model to determine the minimum ratio of measurement to ranking costs (cost ratio) necessary in order for RSS to be as cost effective as SRS for data from the normal, exponential, and lognormal distributions. We consider both equal and unequal RSS allocations and two types of estimators of the mean: the typical distribution-free (DF) estimator and the best linear unbiased estimator (BLUE). The minimum cost ratio necessary for RSS to be as cost effective as SRS depends on the underlying distribution of the data, as well as the allocation and type of estimator used. Most minimum necessary cost ratios are in the range of 1-6, and are lower for BLUEs than for DF estimators. The higher the prior knowledge of the distribution underlying the data, the lower the minimum necessary cost ratio and the more attractive RSS is over SRS. X n i¼1 ð ði:nÞ À Þ 2 , where ði:nÞ and 2 ði:nÞ are the mean and variance of the ith order statistic from a set of size n. Note that all observations come from different sets and hence are independent; since the population is continuous, it is assumed that all sampled units are distinct. and show that, for equal sample sizes (nm ¼ k), Varð " X X RSS Þ Varð " X XÞ. Now define the relative precision, RP, of the RSS estimator with respect to the SRS estimator as
2011
Stratified percentile ranked set sampling (SPRSS) method is suggested for estimating the population mean. The SPRSS is compared with the simple random sampling (SRS), stratified simple random sampling (SSRS) and stratified ranked set sampling (SRSS). It is shown that SPRSS estimator is an unbiased estimator of the population mean of symmetric distributions and is more efficient than its counterparts using SRS, SSRS and SRSS based on the same number of measured units.
Improvement in estimating the population mean using two-stage balanced groups ranked set sampling
2010
This study proposes some exponential ratio-type estimators for estimating the population mean of the variable under study, using known values of certain population parameter(s). Under simple random sampling without replacement (SRSWOR) scheme, mean square error (MSE) equations of all proposed estimators are obtained and compared with each other. The theoretical results are supported by a numerical illustration.
An Efficient Class of Estimators for the Finite Population Mean in Ranked Set Sampling
Open Journal of Statistics, 2016
In this paper, we propose a class of estimators for estimating the finite population mean of the study variable under Ranked Set Sampling (RSS) when population mean of the auxiliary variable is known. The bias and Mean Squared Error (MSE) of the proposed class of estimators are obtained to first degree of approximation. It is identified that the proposed class of estimators is more efficient as compared to [1] estimator and several other estimators. A simulation study is carried out to judge the performances of the estimators.
An Application of The Regression Estimator in Ranked Set Sampling
Ranked Set Sampling (RSS) is a sampling method which is a cost-efficient alternative to simple random sampling (SRS). An efficient regression estimator in RSS was proposed by Yu and Lam [11] for estimating the population mean of Y when the population mean of an auxiliary variable is known. In this paper, the regression estimator proposed by Yu and Lam is considered. Then an application with original Turkish data for estimating the population mean and the variance of the population mean is given.