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AL-SALEH MOHAMMAD FRAIWAN,AL-ANANBEH AHMAD MOHAMMAD The Korean Statistical Society 2005 Journal of the Korean Statistical Society Vol.34 No.2
In this paper, we consider the estimation of the correlation coefficient in the bivariate normal distribution, based on a sample obtained using a modification of the moving extreme ranked set sampling technique (MERSS) that was introduced by Al-Saleh and Al-Hadhrami (2003a). The modification involves using a concomitant random variable. Nonparametric-type methods as well as the maximum likelihood estimation are considered under different settings. The obtained estimators are compared to their counterparts that are obtained based simple random sampling (SRS). It appears that the suggested estimators are more efficient
Mohammad Fraiwan AL-Saleh,Ahmad Mohammad Al-Ananbeh 한국통계학회 2005 Journal of the Korean Statistical Society Vol.34 No.2
In this paper, we consider the estimation of the correlation coefficient in the bivariate normal distribution, based on a sample obtained using a modi- fication of the moving extreme ranked set sampling technique (MERSS) that was introduced by Al-Saleh and Al-Hadhrami (2003a). The modification involves using a concomitant random variable. Nonparametric-type methods as well as the maximum likelihood estimation are considered under different settings. The obtained estimators are compared to their counterparts that are obtained based simple random sampling (SRS). It appears that the suggested estimators are more efficient
Other approaches to bivariate ranked set sampling
Al-Saleh, Mohammad Fraiwan,Alshboul, Hadeel Mohammad The Korean Statistical Society 2018 Communications for statistical applications and me Vol.25 No.3
Ranked set sampling, as introduced by McIntyre (Australian Journal of Agriculture Research, 3, 385-390, 1952), dealt with the estimation of the mean of one population. To deal with two or more variables, different forms of bivariate and multivariate ranked set sampling were suggested. For a technique to be useful, it should be easy to implement in practice. Bivariate ranked set sampling, as introduced by Al-Saleh and Zheng (Australian & New Zealand Journal of Statistics, 44, 221-232, 2002), is not easy to implement in practice, because it requires the judgment ranking of each of the combination of the order statistics of the two characteristics. This paper investigates two modifications that make the method easier to use. The first modification is based on ranking one variable and noting the rank of the other variable for one cycle, and do the reverse for another cycle. The second approach is based on ranking of one variable and giving the second variable the same rank (Concomitant Order Statistic) for one cycle and do the reverse for the other cycle. The two procedures are investigated for an estimation of the means of some well-known distributions. It is show that the suggested approaches can be used in practice and can be more efficient than using SRS. A real data set is used to illustrate the procedure.
Valid estimation of odds ratio using two types of moving extreme ranked set sampling
Hani M. Samawi,Mohammad Fraiwan AL-Saleh 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.1
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.
Valid estimation of odds ratio using two types of moving extreme ranked set sampling
Samawi, Hani M.,Al-Saleh, Mohammad Fraiwan 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.1
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.