Moving Extreme Ranked Set Sampling Research Articles

Parametric estimation for the scale parameter for scale distributions using moving extremes ranked set sampling

A modification of ranked set sampling (RSS) called moving extremes ranked set sampling (MERSS) is considered for the estimation of the scale parameter of scale distributions. A maximum likelihood estimator (MLE) is studied and its properties are obtained. We prove the MLE is an equivariant estimator under scale transformation. In order to give more insight into the performance of MERSS with respect to (w.r.t.) simple random sampling (SRS), the asymptotic efficiency of the MLE using MERSS w.r.t. that using SRS is computed for some usual scale distributions. The relative results show that the MLE using MERSS can be real competitors to the MLE using SRS, when the same sample size is used.

Valid estimation of odds ratio using two types of moving extreme ranked set sampling

The paper provides estimation of the odds ratio between two independent groups using two types of Moving Extreme Ranked Set Sampling (MERSS). Theoretical properties of the suggested estimator are derived and compared with its counterpart estimator using simple random sampling (SRS). It is found that the estimator based on MERSS is always valid and has some advantages over that based on SRS. Real data from a level I Trauma center are used to illustrate the procedures developed in this paper.

Bayes Estimation of the Mean of Normal Distribution Using Moving Extreme Ranked Set Sampling

Moving extreme ranked set sampling (MERSS) is one of useful modifications of ranked set sampling (RSS). The method has been investigated by many authors and proved to be a good competitor to simple random sampling (SRS). In this paper, Bayes estimation of the mean of normal distribution based on MERSS is considered and compared with SRS counterpart. The suggested estimators are found to be more efficient than that from SRS.

On estimating the odds using Moving Extreme Ranked Set Sampling

In this paper, the estimation of the odds F / ( 1 − F ) is considered based on Moving Extreme Ranked Set Sampling (MERSS), a variation of Ranked Set Sampling. The suggested estimator based on MERSS is motivated by some of the theoretical properties of the sum of geometric series. The new estimator and the naive estimator based on simple random sample (SRS) are compared via their mean squared errors. It turned out that the estimator based on MERSS is always valid and can have some advantages over that based on SRS. Data from a level I Trauma center are used to illustrate the procedures developed in this paper.

Bayesian Inference on the Variance of Normal Distribution Using Moving Extremes Ranked Set Sampling

Bayesian inference of the variance of the normal distribution is considered using moving extremes ranked set sampling (MERSS) and is compared with the simple random sampling (SRS) method. Generalized maximum likelihood estimators (GMLE), confidence intervals (CI), and different testing hypotheses are considered using simple hypothesis versus simple hypothesis, simple hypothesis versus composite alternative, and composite hypothesis versus composite alternative based on MERSS and compared with SRS. It is shown that modified inferences using MERSS are more efficient than their counterparts based on SRS.

Some Estimators for the Population Mean Using Auxiliary Information Under Ranked Set Sampling

The best linear unbiased estimator (BLUE) for the population mean under moving extreme ranked set sampling (MERSS) is derived for general location and scale parameters of distributions which generalizes Al-Odat and Al-Saleh (2001). It is compared with the sample mean of simple random sampling (SRS). The efficient sample size under the MERSS for which the BLUE estimator dominates the usual sample mean under SRS for estimating the population mean is also computed for several distributions.

Modified inference about the mean of the exponential distribution using moving extreme ranked set sampling

The maximum likelihood estimator (MLE) and the likelihood ratio test (LRT) will be considered for making inference about the scale parameter of the exponential distribution in case of moving extreme ranked set sampling (MERSS). The MLE and LRT can not be written in closed form. Therefore, a modification of the MLE using the technique suggested by Maharota and Nanda (Biometrika 61:601–606, 1974) will be considered and this modified estimator will be used to modify the LRT to get a test in closed form for testing a simple hypothesis against one sided alternatives. The same idea will be used to modify the most powerful test (MPT) for testing a simple hypothesis versus a simple hypothesis to get a test in closed form for testing a simple hypothesis against one sided alternatives. Then it appears that the modified estimator is a good competitor of the MLE and the modified tests are good competitors of the LRT using MERSS and simple random sampling (SRS).

Parametric estimation for the location parameter for symmetric distributions using moving extremes ranked set sampling with application to trees data

AbstractA modification of ranked set sampling (RSS) called moving extremes ranked set sampling (MERSS) is considered parametrically, for the location parameter of symmetric distributions. A maximum likelihood estimator (MLE) and a modified MLE are considered and their properties are studied. Their efficiency with respect to the corresponding estimators based on simple random sampling (SRS) are compared for the case of normal distribution. The method is studied under both perfect and imperfect ranking (with error in ranking). It appears that these estimators can be real competitors to the MLE using (SRS). The procedure is illustrated using tree data. Copyright © 2003 John Wiley & Sons, Ltd.

Estimation of the mean of the exponential distribution using moving extremes ranked set sampling

Moving Extremes Ranked Set Sampling (MERSS) is a useful modification of Ranked Set Sampling (RSS). Unlike RSS, MERSS allows for an increase of set size without introducing too much ranking error. The method is considered parametrically under exponential distribution. Maximum likelihood estimator (MLE), and a modified MLE are considered and their properties are studied. The method is studied under both perfect and imperfect ranking (with error in ranking). It appears that these estimators can be real competitors to the MLE using the usual simple random sampling (SRS).