Bayes Estimation of Weibull Distribution Parameters Using Ranked Set Sampling

Helu, Amal; Abu-Salih, Muhammad S.; Alkam, Osama

doi:10.1080/03610920903061039

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…The paper aligns with the results of Alodat et al, [6] and AlSaleh and AlSharafat [3], emphasizing that Bayesian estimators prove to be more efficient than those derived from simple random sampling. We also noticed a decrease in the standard error (SE) of Bayesian estimates based on MRSS as the sample size m increases, reinforcing findings by De Iorio et al [10] and Helo et al, [14], both of whom demonstrated a similar trend in ranked set sampling and the Bayesian estimation of Weibull parameters respectively. This study assumes a symmetric distribution for random errors.…”

Section: Discussionsupporting

confidence: 88%

Bayesian Regression Analysis using Median Rank Set Sampling

Nawajah,

Kanj,

Kotb

et al. 2024

Eur. J. Pure Appl. Math.

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Bayesian estimation of the linear regression parameter system is considered by deploying Median Rank Set Sampling (MRSS). The full conditional distributions and the associated posterior distribution are obtained. Therefore, based on Markov Chain Monte Carlo simulation, the Bayesian point estimates and credible intervals for the regression parameters are determined. To measure the efficiency of the obtained Bayesian estimates concerning the frequentist estimates we compute the asymptotic relative efficiency of the obtained Bayesian estimates using Markov Chain Monte Carlo simulation.

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Section: Discussionsupporting

confidence: 88%

Bayesian Regression Analysis using Median Rank Set Sampling

Nawajah,

Kanj,

Kotb

et al. 2024

Eur. J. Pure Appl. Math.

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“…Step 4: The approximate confidence intervals of λ and θ under SRS and SRS are obtained by the asymptotic variance covariance matrix using equations (8), equations (16), equations (11) and equations (18), respectively.…”

Section: Simulation Studymentioning

confidence: 99%

Estimation of the Inverse Weibull Parameters Under Ranked Set Sampling

Salem¹,

Nassar²,

Hozaien³

2021

Journal of Data Science

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In this paper we use the maximum likelihood (ML) and the modified maximum likelihood (MML) methods to estimate the unknown parameters of the inverse Weibull (IW) distribution as well as the corresponding approximate confidence intervals. The estimates of the unknown parameters are obtained based on two sampling schemes, namely, simple random sampling (SRS) and ranked set sampling (RSS). Comparison between the different proposed estimators is made through simulation via their mean square errors (MSE), Pitman nearness probability (PN) and confidence length.

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“…It has been widely studied and applied in several fields including reliability, lifetime data analysis, climatology, biology, and engineering. (see, for instance, Helu et al(2010) and the reference therein). This paper deals with the problem of estimating the parameters of Farlie-Gumbel-Morgenstern bivariate Weibull (FGMBW) distribution, which was introduced by Almetwally et al(2020) as a modification of the base Weibull distribution, based on ranked set sampling (RSS) design.…”

Section: Introductionmentioning

confidence: 99%

Estimation based on Ranked Set Sampling for Farlie--Gumbel--Morgenstern Bivariate Weibull Distribution Parameters with an application to medical data

Hanandeh,

Al-omari

2023

Pak.j.stat.oper.res.

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In this article, we address the problem of estimating the parameters of Farlie-Gumbel-Morgenstern bivariate Weibull distribution using ranked set sample (RSS) design. The suggested estimators of the FGMBW distribution parameters are compared with their counterparts based on simple random sampling (SRS) via Monte Carlo simulations studies. An example of a real data set consists of times (in days) to the first and second recurrence of infection for 30 kidney patients is considered for illustration. It turns out that the RSS estimators results in an improvement in efficiency as compared to the simple random sampling estimators based on the same number of measured units for all cases considered in this study.

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Bayes Estimation of Weibull Distribution Parameters Using Ranked Set Sampling

Cited by 16 publications

References 13 publications

Bayesian Regression Analysis using Median Rank Set Sampling

Bayesian Regression Analysis using Median Rank Set Sampling

Estimation of the Inverse Weibull Parameters Under Ranked Set Sampling

Estimation based on Ranked Set Sampling for Farlie--Gumbel--Morgenstern Bivariate Weibull Distribution Parameters with an application to medical data

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