Dependent Ranked Set Sampling Designs for Parametric Estimation with Applications

Sabry, M.; Shaaban, Mostafa

doi:10.1007/s40745-020-00247-3

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…, m} be a neoteric ranked set sample, where m is the set size drawn from a distribution with pdf f (u; θ ) and cdf F(u; θ), where m is the set size and θ is the parameter space. Then, according to Sabry and Shabaan [11], the likelihood function of NRSS samples drawn from Weibull (α, β) is then given by…”

Section: Estimation Based On Nrss Designmentioning

confidence: 99%

“…Moreover, twostage NRSS designs were proposed in [9], where they showed that five different sampling designs based on NRSS outperform RSS and NRSS designs. The likelihood estimation of distribution parameters using DRSS, NRSS, and DNRSS designs were proposed by [10,11], and showed that the proposed likelihood estimators provide similar results as when estimating population means and variances using these designs.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach

Sabry¹,

Almetwally²,

Almongy³

et al. 2021

Computers, Materials &Amp; Continua

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In this paper, a joint analysis consisting of goodness-of-fit tests and Markov chain Monte Carlo simulations are used to assess the performance of some ranked set sampling designs. The Markov chain Monte Carlo simulations are conducted when Bayesian methods with Jeffery's priors of the unknown parameters of Weibull distribution are used, while the goodness of fit analysis is conducted when the likelihood estimators are used and the corresponding empirical distributions are obtained. The ranked set sampling designs considered in this research are the usual ranked set sampling, extreme ranked set sampling, median ranked set sampling, and neoteric ranked set sampling designs. An intensive Monte Carlo simulation study is conducted using Lindley's approximation algorithm to compute the different designs'based estimators. The study showed that the dependent design "neoteric ranked set sampling design" is superior to other ranked set designs and the total relative efficiency is higher than the other designs' total relative efficiency.

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Section: Estimation Based On Nrss Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach

Sabry¹,

Almetwally²,

Almongy³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…For example, [7] studied Weibull and Pareto distributions, [8] introduced the maximum likelihood estimation of the modified Weibull distribution parameters using extreme ranking set sampling, [9], [10] and [11].and a -stage RSS design uses +1 sample units from the target population to produce a sample of size after stages of ranking developed [12]. For more details, see [13], [14], [15] and [16], see more [17], [18] and [19].…”

Section: Figure 1 Pdf Of the Epgw Distribution For Different Parameter Valuesmentioning

confidence: 99%

“…Table 1and Table 2 lists the results of bias and MSE for different estimators, table (3) lists the efficiency results, and Figure (6)(7)(8)(9)(10)(11) shows the simulation results. The following conclusions can be observed from Table (1-2) 1) It is small biases in almost all cases…”

Section: Simulation Studymentioning

confidence: 99%

On Maximum Likelihood Estimators of the Parameters of Three-Parameters Weibull Distribution Using Different Ranked Set Sampling Schemes

Aziz¹,

Shaaban²

2021

CRPASE

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Al-Saleh and Al-Kadiri first proposed double rank set sampling (DRSS). It seems that this ranked set sampling (RSS) modification can reduce the loss of RSS efficiency caused by ranking errors, and it is more effective than RSS and simple random sampling (SRS) to estimate the population mean. The proposed likelihood function is used to estimate the parameters of the three-Parameters Weibull distribution. Based on double ranked set sampling, extreme ranked set sampling, ranked set sampling (RSS) and simple random sampling (SRS) designs, the maximum likelihood estimator (MLE) is compared with the corresponding likelihood estimator. A simulation was carried out and the absolute relative biases, mean square error (MSE) and relative efficiency of different schemes were compared. It is found that, MSEs based SRS data has the largest MSEs comparing to RSS and its modifications schemes. This study revealed that DRSS technique has the superior over the rest of other sampling schemes. In almost all cases, DRSS has the smallest MSEs and largest efficiencies.

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“…The NRSS scheme was suggested by Zamanzade and Al-Omari (2016) and it differs from the original RSS scheme by the composition of a single set of n 2 units instead of n sets of size n. This strategy has been shown to be effective, producing more efficient estimators for the population mean and variance than the SRS and RSS schemes. Several studies have been conducted based on the NRSS scheme by several authors (see, for example, Koyuncu and Karagöz (2018) and Sabry and Shaaban (2020)).…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of exponentiated exponential distribution under selective ranked set sampling

Hassan

Elshaarawy

Nagy

2022

Statistics in Transition New Series

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Partial ranked set sampling (PRSS) is a cost-effective sampling method. It is a combination of simple random sample (SRS) and ranked set sampling (RSS) designs. The PRSS method allows flexibility for the experimenter in selecting the sample when it is either difficult to rank the units within each set with full confidence or when experimental units are not available. In this article, we introduce and define the likelihood function of any probability distribution under the PRSS scheme. The performance of the maximum likelihood estimators is examined when the available data are assumed to have an exponentiated exponential (EE) distribution via some selective RSS schemes as well as SRS. The suggested ranked schemes include the PRSS, RSS, neoteric RSS (NRSS), and extreme RSS (ERSS). An intensive simulation study was conducted to compare and explore the behaviour of the proposed estimators. The study demonstrated that the maximum likelihood estimators via PRSS, NRSS, ERSS, and RSS schemes are more efficient than the corresponding estimators under SRS. A real data set is presented for illustrative purposes.

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Dependent Ranked Set Sampling Designs for Parametric Estimation with Applications

Cited by 9 publications

References 2 publications

Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach

Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach

On Maximum Likelihood Estimators of the Parameters of Three-Parameters Weibull Distribution Using Different Ranked Set Sampling Schemes

Parameter estimation of exponentiated exponential distribution under selective ranked set sampling

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