On distribution selection under ranked set sampling schemes

2022 ◽  
pp. 1-21
Author(s):  
Vinicius Ricardo Riffel ◽  
Cesar Augusto Taconeli ◽  
Paulo Justiniano Ribeiro Junior
Keyword(s):  
Ranked Set Sampling ◽  
Sampling Schemes

scholarly journals A flexible ranked set sampling schemes: Statistical analysis on scale parameter

2020 ◽  
Vol 9 (1) ◽  
pp. 189-203
Author(s):  
Abbas Eftekharian ◽  
Mostafa Razmkhah ◽  
Jafar Ahmadi
Keyword(s):  
Maximum Likelihood ◽  
Existence And Uniqueness ◽  
Sampling Methods ◽  
Scale Parameter ◽  
Likelihood Estimation ◽  
Ranked Set Sampling ◽  
Likelihood Estimator ◽  
Normal Distributions ◽  
Sampling Schemes ◽  
The Cost

A flexible ranked set sampling scheme including some various existing sampling methods  is proposed. This scheme may be used to minimize the  error of ranking and the cost of sampling. Based on the data obtained from this scheme, the maximum likelihood estimation as well as the Fisher information are studied for the  scale family of distributions. The existence and uniqueness of  the  maximum likelihood estimator  of the scale parameter of the exponential  and  normal distributions are  investigated. Moreover, the optimal scheme is derived via simulation and numerical computations.

scholarly journals Maximum likelihood estimation in location-scale families using varied L ranked set sampling

2020 ◽  
Author(s):  
Amer Al-Omari
Keyword(s):  
Maximum Likelihood ◽  
Random Sampling ◽  
Maximum Likelihood Estimators ◽  
Likelihood Estimation ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Sampling Schemes ◽  
Scale Parameters ◽  
Insight Into ◽  
Family Of Distributions

Recently, a generalized ranked set sampling (RSS) scheme has been introduced which encompasses several existing RSS schemes, namely varied L RSS (VLRSS), and it provides more precise estimators of the population mean than the estimators with the traditional simple random sampling (SRS) and RSS schemes. In this paper, we extend the work and consider the maximum likelihood estimators (MLEs) of the location and scale parameters when sampling from a location-scale family of distributions. In order to give more insight into the performance of VLRSS with respect to SRS and RSS schemes, the asymptotic relative precisions of the MLEs using VLRSS relative to that using SRS and RSS are compared for some usual location-scale distributions. It turns out that the MLEs with VLRSS are more precise than those with the existing sampling schemes.

scholarly journals Comparison of two sampling schemes in estimating the stress-strength reliability under the proportional reversed hazard rate model

2020 ◽  
Vol 9 (1) ◽  
pp. 82-98
Author(s):  
Amineh Sadeghpour ◽  
Ahmad Nezakati ◽  
Mahdi Salehi
Keyword(s):  
Hazard Rate ◽  
Interval Estimation ◽  
Minimum Variance ◽  
Ranked Set Sampling ◽  
Sampling Scheme ◽  
Rate Model ◽  
Data Set ◽  
Reversed Hazard Rate ◽  
Sampling Schemes ◽  
Hazard Rate Model

In this paper, point and interval estimation of stress-strength reliability based on lower record ranked set sampling scheme under the proportional reversed hazard rate model are considered. Maximum likelihood, uniformly minimum variance unbiased, and Bayesian estimators of $\mathcal{R}$ are derived and their performances are compared. Various confidence intervals for the parameter $\mathcal{R}$ are constructed, and compared based on the simulation study. Moreover, the record ranked set sampling scheme is compared with ordinary records in case of interval estimations. Finally, a data set has been analyzed for illustrative purposes.

Modified ranked set sampling schemes for estimating the population mean

2019 ◽  
Vol 22 (8) ◽  
pp. 1481-1497
Author(s):  
Lakhkar Khan ◽  
Javid Shabbir ◽  
Alamgir Khalil
Keyword(s):  
Ranked Set Sampling ◽  
Population Mean ◽  
Sampling Schemes

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.

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