On the BLUE of the Population Mean for Location and Scale Parameters of Distributions Based on Moving Extreme 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

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

Auxiliary information is used along with ranking information to derive several classes of estimators to estimate the population mean of a variable of interest based on RSS (ranked set sample). The properties of these newly suggested estimators were examined. Comparisons between special cases of these estimators and other known estimators are made using a real data set. Some of the new estimators are superior to the old ones in terms of bias and mean square error

Local Power For Combining Independent Tests in The Presence of Nuisance Parameters For The Logistic Distribution

Four combination methods of independent tests for testing a simple hypothesis versus one-sided alternative are considered viz. Fisher, the logistic, the sum of P-values and the inverse normal method in case of logistic distribution. These methods are compared via local power in the presence of nuisance parameters for some values of α using simple random sample

On Distribution Function Estimation Using Double Ranked Set Samples With Application

As a variation of ranked set sampling (RSS); double ranked set sampling (DRSS) was introduced by Al-Saleh and Al-Kadiri (2000), and it has been used only for estimating the mean of the population. In this paper DRSS will be used for estimating the distribution function (cdf). The efficiency of the proposed estimators will be obtained when ranking is perfect. Some inference on the distribution function will be drawn based on Kolomgrov-Smirnov statistic. It will be shown that using DRSS will increase the efficiency in this case

Combining independent tests of triangular distribution

The problem of combining n independent tests, as n --> [is proportional to], for testing that the variables are uniformly distributed over the interval (0, 1) vs. that they have a triangular distribution with pdf will be studied. Six popular omnibus methods will be compared via Bahadur efficiency. It will be shown numerically that the sum of p-values method is the best among the methods studied.Triangular distribution Combining independent tests Omnibus methods Bahadur efficiency