Estimation based on ranked set sampling for the two-parameter Birnbaum–Saunders distribution

Pedroso, Vinicius César; Taconeli, César Augusto; Giolo, Suely Ruiz

doi:10.1080/00949655.2020.1814287

Cited by 19 publications

(6 citation statements)

References 25 publications

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“…For more research on RSS-based reliability estimate [19,20,21,22,23]. Further findings on RSS-based parametric estimation encompass several estimation techniques [24,25,26,27,28,29]. Probability distribution models are essential and widely utilized in many domains, including physics, medicine, business management, engineering, etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal Estimation of Power Chris-Jerry DistributionParameters Using Ranked Set Sampling Design withApplication

Alsadat,

Hassan,

Elgarhy

et al. 2024

Preprint

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Effective sample design has a major role in the quality of parameter estimation in statisticalparameter estimation issues. The ranking set sampling (RSS) strategy is effective and a less costlyoption than simple random sampling (SRS). A novel mixture continuous lifetime distribution thathas been proposed recently is the power Chris-Jerry distribution (PC-JD). It is useful for modelinga number of real data sets. This paper investigates the RSS approach for estimating the PC-JD’sparameters. There are roughly sixteen different techniques of estimation that are used, such as themaximum likelihood method, the percentiles method, some methods based on minimum distance,the Kolmogorov method, and some methods based on minimum and maximum spacing distances. Incomparison to a SRS, the simulation research assesses the performance of the suggested RSS-basedestimates in terms of some measures of accuracy. To identify the optimal estimating strategy, thepartial and overall ranks of many estimates are shown. According to numerical results, the maximumlikelihood approach seems to be quite beneficial in evaluating the estimated quality of RSS and SRS.RSS is a more effective sampling approach than SRS owing to its better efficiency. Additionally, thedifferent estimation techniques with survival data for both sampling techniques are examined

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Section: Introductionmentioning

confidence: 99%

Optimal Estimation of Power Chris-Jerry DistributionParameters Using Ranked Set Sampling Design withApplication

Alsadat,

Hassan,

Elgarhy

et al. 2024

Preprint

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“…Joukar et al [24] obtained an estimation of parameters for the exponential Poisson distribution based on RSS. Pedroso et al [25] discussed parameter estimation for the Birnbaum-Saunders distribution based on RSS. Biradar [26] discussed the estimation of the parameters of the location scale family of distributions based on RSS with unequal sample sizes.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Gumbel Distribution Based on Ordered Maximum Ranked Set Sampling with Unequal Samples

Hassan,

Alamri

2024

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Sample selection is one of the most important factors in estimating the unknown parameters of distributions, as it saves time, saves effort, and gives the best results. One of the challenges is deciding on a suitable distribution estimate technique and adequate sample selection to provide the best results in comparison with earlier research. The method of moments (MOM) was decided on to estimate the unknown parameters of the Gumbel distribution, but with four changes in the sample selection, which were simple random sample (SRS), ranked set sampling (RSS), maximum ranked set sampling (MRSS), and ordered maximum ranked set sampling (OMRSS) techniques, due to small sample sizes. The MOM is a traditional method for estimation, but it is difficult to use when dealing with RSS modification. RSS modification techniques were used to improve the efficiency of the estimators based on a small sample size compared with the usual SRS estimator. A Monte Carlo simulation study was carried out to compare the estimates based on different sampling. Finally, two datasets were used to demonstrate the adaptability of the Gumbel distribution based on the different sampling techniques.

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“…[52] for the generalized inverse Lindley distribution, Ref. [53] for the Birnbaum-Saunders distribution, Ref. [54] for the generalized quasi-Lindley distribution, Ref.…”

Section: Introductionmentioning

confidence: 99%

Bayesian and Non-Bayesian Estimation for a New Extension of Power Topp–Leone Distribution under Ranked Set Sampling with Applications

Alotaibi

Al-Moisheer

Elbatal

et al. 2023

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In this article, we intend to introduce and study a new two-parameter distribution as a new extension of the power Topp–Leone (PTL) distribution called the Kavya–Manoharan PTL (KMPTL) distribution. Several mathematical and statistical features of the KMPTL distribution, such as the quantile function, moments, generating function, and incomplete moments, are calculated. Some measures of entropy are investigated. The cumulative residual Rényi entropy (CRRE) is calculated. To estimate the parameters of the KMPTL distribution, both maximum likelihood and Bayesian estimation methods are used under simple random sample (SRS) and ranked set sampling (RSS). The simulation study was performed to be able to verify the model parameters of the KMPTL distribution using SRS and RSS to demonstrate that RSS is more efficient than SRS. We demonstrated that the KMPTL distribution has more flexibility than the PTL distribution and the other nine competitive statistical distributions: PTL, unit-Gompertz, unit-Lindley, Topp–Leone, unit generalized log Burr XII, unit exponential Pareto, Kumaraswamy, beta, Marshall-Olkin Kumaraswamy distributions employing two real-world datasets.

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Estimation based on ranked set sampling for the two-parameter Birnbaum–Saunders distribution

Cited by 19 publications

References 25 publications

Optimal Estimation of Power Chris-Jerry DistributionParameters Using Ranked Set Sampling Design withApplication

Optimal Estimation of Power Chris-Jerry DistributionParameters Using Ranked Set Sampling Design withApplication

Estimation of Gumbel Distribution Based on Ordered Maximum Ranked Set Sampling with Unequal Samples

Bayesian and Non-Bayesian Estimation for a New Extension of Power Topp–Leone Distribution under Ranked Set Sampling with Applications

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