Abstract: Stratified double extreme ranked set sampling
(SDERSS) method is introduced and considered for estimating the
population mean. The SDERSS is compared with the simple random
sampling (SRS), stratified ranked set sampling (SRSS) and stratified
simple set sampling (SSRS). It is shown that the SDERSS estimator
is an unbiased of the population mean and more efficient than the
estimators using SRS, SRSS and SSRS when the underlying
distribution of the variable of interest is symmetric or asymmetric.
Abstract: Many works have been carried out to compare the
efficiency of several goodness of fit procedures for identifying
whether or not a particular distribution could adequately explain a
data set. In this paper a study is conducted to investigate the power
of several goodness of fit tests such as Kolmogorov Smirnov (KS),
Anderson-Darling(AD), Cramer- von- Mises (CV) and a proposed
modification of Kolmogorov-Smirnov goodness of fit test which
incorporates a variance stabilizing transformation (FKS). The
performances of these selected tests are studied under simple
random sampling (SRS) and Ranked Set Sampling (RSS). This
study shows that, in general, the Anderson-Darling (AD) test
performs better than other GOF tests. However, there are some
cases where the proposed test can perform as equally good as the
AD test.