scholarly journals The Efficiency Comparison and Application of Proposed Rhombus Ranked Set Sampling

2021 ◽  
pp. 65-78
Author(s):  
Mishal Choudri ◽  
Nadia Saeed ◽  
Kanwal Saleem
Keyword(s):  
Probability Distributions ◽  
Real Life ◽  
Secondary Data ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Population Means ◽  
Zn Concentration ◽  
The Family ◽  
Efficiency Comparison ◽  
Extreme Ranked Set Sampling

A new scheme ‘Rhombus Ranked Set Sampling’ (RRSS) is developed in this research together with its properties for estimating the population means. Mathematical validation along with the simulation evaluation is presented. The proposed method is an addition to the family of different sampling methods and generalization of ‘Folded Ranked Set Sampling’ (FRSS). For the simulation process, nine probability distributions are considered for the efficiency comparison of proposed scheme from which four are symmetric and rest are asymmetric among which Weibull and beta distributions which are used twice, unlike parametric values. (Al-Naseer, 2007 and Bani-Mustafa, 2011). Through simulation processes, it is observed that RRSS is competent and more reliable relative to simple random sampling (SRS), ranked set sampling (RSS) and folded ranked set sampling (FRSS). It is noted that for all the underlying distributions, an increase in the efficiency of Rhombus Ranked Set Sampling (RRSS) is achieved via increasing the size of the sample ‘p’. Besides the efficiency comparison, consistency of the proposed method is also valued by using Co-efficient of Variation (CV).  Secondary data on zinc (Zn) concentration and lead (Pb) contamination in different parts and tissues of freshwater fish was collected to illustrate the evaluation of RRSS against SRS, RSS, FRSS and ERSS (extreme ranked set sampling). The results obtained through real life illustration defend the simulation study and hence indicates that the RRSS estimator is efficient substitute for existing methods (Al-Omari, 2011).

scholarly journals Permutation Tests for Two-sample Location Problem Under Extreme Ranked Set Sampling

2020 ◽  
pp. 387-408
Author(s):  
Monjed H. Samuh ◽  
Ridwan A. Sanusi
Keyword(s):  
Statistical Power ◽  
Permutation Test ◽  
Real Life ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Test Statistics ◽  
Empirical Coverage ◽  
Expected Length ◽  
Permutation Problem ◽  
Extreme Ranked Set Sampling

In this paper, permutation test of comparing two-independent samples is investigated in the context of extreme ranked set sampling (ERSS). Three test statistics are proposed. The statistical power of these new test statistics are evaluated numerically. The results are compared with the statistical power of the classical independent two-sample $t$-test, Mann-Whitney $U$ test, and the usual two-sample permutation test under simple random sampling (SRS). In addition, the method of computing a confidence interval for the two-sample permutation problem under ERSS is explained. The performance of this method is compared with the intervals obtained by SRS and Mann-Whitney procedures in terms of empirical coverage probability and expected length. The comparison shows that the proposed statistics outperform their counterparts. Finally, the application of the proposed statistics is illustrated using a real life example.

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 Bayesian Inference for the Parameters of Kumaraswamy Distribution via Ranked Set Sampling

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1170
Author(s):  
Huanmin Jiang ◽  
Wenhao Gui
Keyword(s):  
Real Life ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Interval Length ◽  
Kumaraswamy Distribution ◽  
Credible Intervals ◽  
Average Confidence ◽  
Estimated Risk ◽  
Interval Estimators ◽  
Point Estimators

In this paper, we address the estimation of the parameters for a two-parameter Kumaraswamy distribution by using the maximum likelihood and Bayesian methods based on simple random sampling, ranked set sampling, and maximum ranked set sampling with unequal samples. The Bayes loss functions used are symmetric and asymmetric. The Metropolis-Hastings-within-Gibbs algorithm was employed to calculate the Bayes point estimates and credible intervals. We illustrate a simulation experiment to compare the implications of the proposed point estimators in sense of bias, estimated risk, and relative efficiency as well as evaluate the interval estimators in terms of average confidence interval length and coverage percentage. Finally, a real-life example and remarks are presented.

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).  

On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design

2022 ◽  
pp. 42-61
Author(s):  
Agustin Santiago Moreno ◽  
Khalid Ul Islam Rather
Keyword(s):  
Empirical Evidence ◽  
Simulation Study ◽  
Random Sampling ◽  
Relative Efficiency ◽  
Convex Combination ◽  
Real Data ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Population Means ◽  
Sampling Schemes

In this chapter, the authors consider the problem of estimating the population means of two sensitive variables by making use ranked set sampling. The final estimators are unbiased and the variance expressions that they derive show that ranked set sampling is more efficient than simple random sampling. A convex combination of the variance expressions of the resultant estimators is minimized in order to suggest optimal sample sizes for both sampling schemes. The relative efficiency of the proposed estimators is then compared to the corresponding estimators for simple random sampling based on simulation study and real data applications. SAS codes utilized in the simulation to collect the empirical evidence and application are included.

scholarly journals Generalized Exponential Distribution - Estimation of parameters using modifications in methods of Ranked Set Sampling

2021 ◽  
Author(s):  
Vyomesh Prahlad Nandurbarkar ◽  
Ashok Shanubhogue
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Exponential Distribution ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Likelihood Method ◽  
Generalized Exponential Distribution ◽  
Data Set ◽  
Extreme Ranked Set Sampling

Abstract In this study, we estimate the parameters of the Generalized Exponential Distribution using Moving Extreme Ranked Set Sampling (MERSS). Using the maximum likelihood estimation method, we derive the expressions. MERSS estimates are compared with estimates obtained by simple random sampling (SRS) using a real data set. We also study the other variations of the methods of Ranked Set Sampling like Quartile Ranked Set Sampling(QRSS), Median Ranked Set Sampling(MRSS) and Flexible Ranked Set Sampling(FLERSS) (a scheme based on QRSS and MRSS). For known shape parameter values, we present coefficients for linear combinations of order statistics for least squares estimates. Here, the expressions are derived through maximum likelihood, and the estimates are calculated numerically. Simulated results indicate that estimates generated using least-squares and the maximum likelihood method for Ranked Set Sampling (RSS) perform better than those generated using Simple Random Sampling (SRS). Asymptotically, MERSS outperforms SRS, QRSS, MRSS, and FLERSS.

scholarly journals Pengaruh Lingkungan Keluarga terhadap Penyalahgunaan NAPZA pada Remaja

2017 ◽  
Vol 5 (1) ◽  
pp. 80
Author(s):  
Dwi Oktavia Sri Asmoro ◽  
Soenarnatalina Melaniani
Keyword(s):  
Family Environment ◽  
Drug Users ◽  
Secondary Data ◽  
Simple Random Sampling ◽  
Observational Research ◽  
Young Generation ◽  
Cross Sectional ◽  
Environment Factors ◽  
Study Results ◽  
The Family

The number of drug users was increasing. Drug abuse is regarded as one. Drug user regarded as a threat that sooner or later can destroy the young generation. The research was aimed to analized the infl uence of family environmental factors toward to abuse juvenile drug in Surabaya. The research was observational research by design cross sectional. Technique sampling was simple random sampling, with sampling teenager was 10-19 year drug users who performs outpatient in BNN throughout Surabaya as many as 53 people. The study was done at December 2015. This research used obtained through primary and secondary data, the analysis used binary regression logistic techniques. The study results show that the infl uence of family environment that is harmonious family (pvalue = 0,026; or = 6,179), bustle parents (pvalue = 0,032; or = 5,677), parents permissive (p = 0.015; or = 8,001 ), Religious in the family (pvalue = much as 0.021; or = 6,401) and religious in the family are the infl uence on the environment factors (pvalue = 0.021; or = 6,401; CI = 1,321 – 31,025). Religious in the family are the most influence in the family environment factors. It was recommended parents need to create family bonds strong via relationship emotional and taste emphatic on child.

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.

On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design

2022 ◽  
pp. 104-140
Author(s):  
Shivacharan Rao Chitneni ◽  
Stephen A. Sedory ◽  
Sarjinder Singh
Keyword(s):  
Empirical Evidence ◽  
Simulation Study ◽  
Random Sampling ◽  
Relative Efficiency ◽  
Convex Combination ◽  
Real Data ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Population Means ◽  
Sampling Schemes

In the chapter, the authors consider the problem of estimating the population means of two sensitive variables by making use of ranked set sampling. The final estimators are unbiased and the variance expressions that they derive show that ranked set sampling is more efficient than simple random sampling. A convex combination of the variance expressions of the resultant estimators is minimized in order to suggest optimal sample sizes for both sampling schemes. The relative efficiency of the proposed estimators is then compared to the corresponding estimators for simple random sampling based on simulation study and real data applications. SAS codes utilized in the simulation to collect the empirical evidence and application are included.

Efficient Estimation of a Scale Parameter of Bivariate Lomax Distribution by Ranked Set Sampling

2021 ◽  
pp. 000806832199252
Author(s):  
Rohan D. Koshti ◽  
Kirtee K. Kamalja
Keyword(s):  
Scale Parameter ◽  
Real Life ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Efficient Estimation ◽  
Study Variable ◽  
Variable Formula ◽  
Lomax Distribution ◽  
Efficiency Comparison ◽  
Ams Classification

Ranked set sampling (RSS) is an efficient technique for estimating parameters and is applicable whenever ranking on a set of sampling units can be done easily by a judgment method or based on an auxiliary variable. In this paper, we assume [Formula: see text]to have bivariate Lomax distribution where a study variable [Formula: see text]is difficult and/or expensive to measure and is correlated with an auxiliary variable [Formula: see text] which is readily measurable. The auxiliary variable is used to rank the sampling units. In this article, we propose an estimator for the scale parameter of bivariate Lomax distribution using some of the modified RSS schemes. Efficiency comparison of the proposed estimators is performed numerically as well as graphically. A simulation study is also performed to demonstrate the performance of the proposed estimators. Finally, we implement the results to real-life datasets. AMS classification codes: 62D05, 62F07, 62G30

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