scholarly journals Variance estimation in ranked set sampling using a concomitant variable

2015 ◽  
Vol 105 ◽  
pp. 1-5 ◽  
Author(s):  
Ehsan Zamanzade ◽  
Michael Vock
Keyword(s):  
Variance Estimation ◽  
Ranked Set Sampling ◽  
Concomitant Variable

Mean estimate in ranked set sampling using a length-biased concomitant variable

2018 ◽  
Vol 48 (12) ◽  
pp. 2917-2931
Author(s):  
Chang Cui ◽  
Tao Li ◽  
Lei Zhang
Keyword(s):  
Ranked Set Sampling ◽  
Concomitant Variable

scholarly journals Variance estimation of persons infected with AIDS under ranked set sampling

2019 ◽  
Vol 7 (1) ◽  
pp. 17-21
Author(s):  
Carlos N. Bouza ◽  
Jose F. García ◽  
Gajendra K. Vishwakarma ◽  
Sayed M. Zeeshan
Keyword(s):  
Variance Estimation ◽  
Ranked Set Sampling

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).  

Fuzzy-Weighted Ranked Set Sampling Method

2022 ◽  
pp. 190-208
Author(s):  
Bekir Cetintav ◽  
Selma Gürler ◽  
Neslihan Demirel
Keyword(s):  
Data Collection ◽  
Fuzzy Set ◽  
Sampling Method ◽  
Scientific Research ◽  
Ranked Set Sampling ◽  
Advanced Method ◽  
Concomitant Variable ◽  
Exact Measurement ◽  
Ranking Information ◽  
Ranking Mechanism

Sampling method plays an important role for data collection in a scientific research. Ranked set sampling (RSS), which was first introduced by McIntyre, is an advanced method to obtain data for getting information and inference about the population of interest. The main impact of RSS is to use the ranking information of the units in the sampling mechanism. Even though most of theoretical inferences are made based on exact measurement of the variable of interest, the ranking process is done with an expert judgment or concomitant variable (without exact measurement) in practice. Because of the ambiguity in discriminating the rank of one unit with another, ranking the units could not be perfect, and it may cause uncertainty. There are some studies focused on the modeling of this uncertainty with a probabilistic perspective in the literature. In this chapter, another perspective, a fuzzy-set-inspired approach, for the uncertainty in the ranking mechanism of RSS is introduced.

scholarly journals Inference on Downton’s Bivariate Exponential Distribution Based on Moving Extreme Ranked Set Sampling

2016 ◽  
Vol 42 (3) ◽  
pp. 161-179 ◽  
Author(s):  
Ahmed Ali Hanandeh ◽  
Mohammad Fraiwan Al-Saleh
Keyword(s):  
Exponential Distribution ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Bivariate Exponential Distribution ◽  
Linear Combinations ◽  
Unbiased Estimators ◽  
Concomitant Variable ◽  
Bivariate Exponential ◽  
Extreme Ranked Set Sampling

The purpose of this paper is to estimate the parameters of Downton’sbivariate exponential distribution using moving extreme ranked set sampling(MERSS). The estimators obtained are compared via their biases andmean square errors to their counterparts using simple random sampling (SRS).Monte Carlo simulations are used whenever analytical comparisons are difficult.It is shown that these estimators based on MERSS with a concomitantvariable are more efficient than the corresponding ones using SRS. Also,MERSS with a concomitant variable is easier to use in practice than RSS witha concomitant variable. Furthermore, the best unbiased estimators among allunbiased linear combinations of the MERSS elements are derived for someparameters.

Estimation of the means of the bivariate normal using moving extreme ranked set sampling with concomitant variable

2007 ◽  
Vol 48 (2) ◽  
pp. 179-195 ◽  
Author(s):  
Mohammad Fraiwan Al-Saleh ◽  
Ahmad Mohammad Al-Ananbeh
Keyword(s):  
Ranked Set Sampling ◽  
Bivariate Normal ◽  
Concomitant Variable ◽  
Extreme Ranked Set Sampling

Variance Estimation of Persons Infected With AIDS Under Ranked Set Sampling

2019 ◽  
pp. 281-290
Author(s):  
Carlos N. Bouza-Herrera ◽  
Jose F. García ◽  
Gajendra K. Vishwakarma ◽  
Sayed Mohammed Zeeshan
Keyword(s):  
Variance Estimation ◽  
Ranked Set Sampling

Environmental sampling with a concomitant variable: A comparison between ranked set sampling and stratified simple random sampling

1996 ◽  
Vol 23 (2-3) ◽  
pp. 231-256 ◽  
Author(s):  
Amarjot Kaur ◽  
G. P. Patil ◽  
Susan J. Shirk ◽  
Charles Taillie
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Environmental Sampling ◽  
Concomitant Variable
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