Estimation of the population mean by successive use of an auxiliary variable in median ranked set sampling

2020 ◽  
pp. 1-24
Author(s):  
Usman Shahzad ◽  
Ishfaq Ahmad ◽  
Evrim Oral ◽  
Muhammad Hanif ◽  
Ibrahim Mufrah Almanjahie
Keyword(s):  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Population Mean ◽  
Median Ranked Set Sampling

scholarly journals Regression Estimator Using Double Ranked Set Sampling

2002 ◽  
Vol 7 (2) ◽  
pp. 311
Author(s):  
Hani M. Samawi ◽  
Eman M. Tawalbeh
Keyword(s):  
Correlation Coefficient ◽  
Real Data ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Regression Estimator ◽  
Primary Analysis ◽  
Data Set ◽  
Population Mean ◽  
The Mean

The performance of a regression estimator based on the double ranked set sample (DRSS) scheme, introduced by Al-Saleh and Al-Kadiri (2000), is investigated when the mean of the auxiliary variable X is unknown. Our primary analysis and simulation indicates that using the DRSS regression estimator for estimating the population mean substantially increases relative efficiency compared to using regression estimator based on simple random sampling (SRS) or ranked set sampling (RSS) (Yu and Lam, 1997) regression estimator.  Moreover, the regression estimator using DRSS is also more efficient than the naïve estimators of the population mean using SRS, RSS (when the correlation coefficient is at least 0.4) and DRSS for high correlation coefficient (at least 0.91.) The theory is illustrated using a real data set of trees.  

RATIO ESTIMATION OF THE POPULATION MEAN USING AUXILIARY INFORMATION UNDER THE OPTIMAL SAMPLING DESIGN

2020 ◽  
pp. 1-12
Author(s):  
Chunxian Long ◽  
Wangxue Chen ◽  
Rui Yang ◽  
Dongsen Yao
Keyword(s):  
Sampling Design ◽  
Auxiliary Information ◽  
Cost Effective ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Study Variable ◽  
Population Mean ◽  
Optimal Sampling Design ◽  
Extreme Ranked Set Sampling

Cost-effective sampling design is a problem of major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time-consuming. In this article, we investigate ratio-type estimators of the population mean of the study variable, involving either the first or the third quartile of the auxiliary variable, using ranked set sampling (RSS) and extreme ranked set sampling (ERSS) schemes. The properties of the estimators are obtained. The estimators in RSS and ERSS are compared to their counterparts in simple random sampling (SRS) for normal data. The numerical results show that the estimators in RSS and ERSS are significantly more efficient than their counterparts in SRS.

scholarly journals On Regression Estimators Using Extreme Ranked Set Samples

2004 ◽  
Vol 9 ◽  
pp. 67
Author(s):  
Hani M. Samawi ◽  
Ahmed Y.A. Al-Samarraie ◽  
Obaid M. Al-Saidy
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Population Mean ◽  
Concomitant Variable ◽  
Regression Estimators ◽  
Monte Carlo Evaluation ◽  
Extreme Ranked Set Sampling

Regression is used to estimate the population mean of the response variable, , in the two cases where the population mean of the concomitant (auxiliary) variable, , is known and where it is unknown. In the latter case, a double sampling method is used to estimate the population mean of the concomitant variable. We invesitagate the performance of the two methods using extreme ranked set sampling (ERSS), as discussed by Samawi et al. (1996). Theoretical and Monte Carlo evaluation results as well as an illustration using actual data are presented. The results show that if the underlying joint distribution of and  is symmetric, then using ERSS to obtain regression estimates is more efficient than using ranked set sampling (RSS) or  simple random sampling (SRS).  

scholarly journals Even Order Ranked Set Sampling with Auxiliary Variable

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Muhammad Tayyab ◽  
Muhammad Noor ul-Amin ◽  
Muhammad Hanif
Keyword(s):  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Sampling Scheme ◽  
Ratio Estimator ◽  
Study Variable ◽  
Median Ranked Set Sampling ◽  
Even Order ◽  
Efficient Alternative ◽  
The One

Even order ranked set sampling (EORSS) is a novel proposed ranked set sampling scheme connected with an auxiliary variable correlated with the study variable. This scheme quantifies only the one sampling unit which is at even position from each ranking set by employing specific criteria. The performance of the ratio estimator under EORSS is compared to its contemporary estimators in simple random sampling (SRS), ranked set sampling (RSS), median ranked set sampling (MRSS) and quartile ranked set sampling (QRSS) exploiting the same number of quantified units. The simulation results proved that EORSS is an efficient alternative sampling scheme for ratio estimation than SRS, RSS, MRSS and QRSS.

scholarly journals Generalized Chain Regression-cum-Chain Ratio Estimator for Population Mean under Stratified Extreme-cum-Median Ranked Set Sampling

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Asad Ali ◽  
Muhammad Moeen Butt ◽  
Muhammad Zubair
Keyword(s):  
Real Life ◽  
Auxiliary Information ◽  
Ranked Set Sampling ◽  
Efficient Estimation ◽  
Ratio Estimator ◽  
Auxiliary Variables ◽  
Study Variable ◽  
Data Set ◽  
Population Mean ◽  
Median Ranked Set Sampling

Estimation of population mean of study variable Y suffers loss of precision in the presence of high variation in the data set. The use of auxiliary information incorporated in construction of an estimator under ranked set sampling scheme results in efficient estimation of population mean. In this paper, we propose an efficient generalized chain regression-cum-chain ratio type estimator to estimate finite population mean of study variable under stratified extreme-cum-median ranked set sampling utilizing information on two auxiliary variables. Mean square error (MSE) of the proposed generalized estimator is derived up to first order of approximation. The applications of the proposed estimator under symmetrical and asymmetrical probability distributions are discussed using simulation study and real-life data set for comparisons of efficiency. It is concluded that the proposed generalized estimator performs efficiently as compared to some existing estimators. It is also observed that the efficiency of the proposed estimator is directly proportional to the correlations between the study variable and its auxiliary variables.

scholarly journals Modified Ratio Estimators of Population Mean Based on Ranked Set Sampling

2019 ◽  
pp. 445-449 ◽  
Author(s):  
Zahid Khan ◽  
Muhammad Ismail
Keyword(s):  
Mean Square Error ◽  
Coefficient Of Variation ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Variation Coefficient ◽  
Mean Square ◽  
Population Mean ◽  
The Mean ◽  
Ratio Estimators ◽  
Coefficient Of Skewness

In this paper, we propose modified ratio estimators using some known values of coefficient of variation, coefficient of skewness and coefficient of kurtosis of auxiliary variable under ranked set sampling (RSS).  The mean square error (MSE) of the proposed ratio estimators under ranked set sampling is derived and compared with some existing ratio estimators under RSS. Through this comparison, we prove theoretically that MSC of proposed estimators is less than some existing ratio estimators in RSS under some conditions. The MSE of proposed estimators along with some existing estimator are also calculated numerically. We observe from numerical results that the suggested ratio estimators are more efficient than some existing ratio estimators under RSS.

scholarly journals Estimation of Population Mean by Using a Generalized Family of Estimators Under Classical Ranked Set Sampling

2021 ◽  
Vol 8 (1) ◽  
pp. 1948184
Author(s):  
Asad Ali ◽  
Muhammad Moeen Butt ◽  
Kanwal Iqbal ◽  
Muhammad Hanif ◽  
Muhammad Zubair
Keyword(s):  
Ranked Set Sampling ◽  
Population Mean

On Two Novel Applications of Ranked Set Sampling

2003 ◽  
Vol 54 (1-2) ◽  
pp. 105-114
Author(s):  
Sukuman Sarikavanij ◽  
Montip Tiensuw
Keyword(s):  
Case Studies ◽  
Random Sample ◽  
Ranked Set Sampling ◽  
Simple Random Sample ◽  
Single Family ◽  
Ranked Set Sample ◽  
Population Mean ◽  
Sales Data ◽  
Tree Data ◽  
Novel Applications

In this paper we discuss two case studies which clearly indicate the advantages of using a ranked set sample (RSS) over those of a simple random sample (SRS). The applications of RSS considered here cover single family homes sales data, and tree data. It is demonstrated that in each case RSS is much more efficient than SRS for estimation of population mean.

Sign in / Sign up

Export Citation Format

Share Document