Product of spacing estimation of entropy for inverse Weibull distribution under progressive type-II censored data with applications

Okasha, Hassan M.; Nassar, Mazen

doi:10.1080/16583655.2022.2046945

Cited by 8 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In most cases, the estimators of the considered entropy measurements based on MLEs outperform their counterparts based on MPSEs in terms of biases and RMSEs. This observation was remarked by [13] when they performed a similar study but based on conventional PT-IIC data.…”

mentioning

confidence: 65%

“…In reality, both the entropy and Φ are unknown. Due to this, the estimation of the parameters and entropy has received the most attention several research papers, including, but not limited to, Wong and Chen [5], Baratpour et al [6], Morabbi and Razmkhah [7], Abo-Eleneen [8], Cramer and Bagh [9], Cho et al [10], Hassan and Zaky [11], Bantan et al [12], and Okasha and Nassar [13]. In the next two subsections, some detailed descriptions of preliminary concepts used in this study are presented.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Entropy Estimation of Inverse Weibull Distribution under Improved Adaptive Progressively Type-II Censoring with Applications

Alam,

Nassar

2023

Axioms

Self Cite

View full text Add to dashboard Cite

This article utilizes improved adaptive progressively Type-II censored data to estimate the entropy of the inverse Weibull distribution. Rényi, q, and Shannon entropy measurements are used to define entropy to achieve this objective. Both point and interval estimations of the entropy quantities are investigated through the maximum likelihood and maximum product of spacing methods. Two parametric bootstrap confidence intervals based on the two estimation techniques are also considered for the various entropy measures. A Monte Carlo simulation study is conducted to investigate how estimates behave at various sample sizes and different censoring schemes based on some statistical measurements. The simulations demonstrate that, as anticipated, when the sample size grows, the estimation accuracy also grows. Furthermore, they show that the estimated entropy measures get closer to the actual entropy values when the censoring level decreases. For purposes of explanation, two applications to actual datasets are taken into consideration. The results verified that the adaptive or improved adaptive progressive censoring schemes give more information about data than the conventional progressive censoring scheme in terms of minimum entropy measures.

show abstract

mentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

On Entropy Estimation of Inverse Weibull Distribution under Improved Adaptive Progressively Type-II Censoring with Applications

Alam,

Nassar

2023

Axioms

Self Cite

View full text Add to dashboard Cite

show abstract

“…The posterior distribution of the unknown parameters θ and σ can be obtained by combining the likelihood function in (5) with the joint prior distribution provided (10) and by applying the Bayes theorem as follows:…”

Section: Bayesian Estimationmentioning

confidence: 99%

“…Once the 1,000 PT-IIC samples collected, the maximum likelihood and 95% ACI estimates of θ , σ , R(t) and h(t) are calculated utilizing via 'maxLik' package (by Henningsen et al [13]) in R 4.1.2 software. To develop the Bayesian MCMC inferences of the same unknown MOL parameters, by according to the mean and variance of the gamma density, two informative sets of the hyperparameters a i and b i for i = 1, 2 of θ and σ are used namely: prior-1: (8,4,10,10). Following Kundu [14] and Dey et al [15], the given hyperparameter values of a i , b i , i = 1, 2 of the unknown MOL parameters are chosen in such a way that the prior mean becomes the expected value of the corresponding parameter.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Computational Analysis of Novel Extended Lindley Progressively Censored Data

Alotaibi,

Nassar,

Elshahhat

2024

CMES

View full text Add to dashboard Cite

A novel extended Lindley lifetime model that exhibits unimodal or decreasing density shapes as well as increasing, bathtub or unimodal-then-bathtub failure rates, named the Marshall-Olkin-Lindley (MOL) model is studied. In this research, using a progressive Type-II censored, various inferences of the MOL model parameters of life are introduced. Utilizing the maximum likelihood method as a classical approach, the estimators of the model parameters and various reliability measures are investigated. Against both symmetric and asymmetric loss functions, the Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) technique with the assumption of independent gamma priors. From the Fisher information data and the simulated Markovian chains, the approximate asymptotic interval and the highest posterior density interval, respectively, of each unknown parameter are calculated. Via an extensive simulated study, the usefulness of the various suggested strategies is assessed with respect to some evaluation metrics such as mean squared errors, mean relative absolute biases, average confidence lengths, and coverage percentages. Comparing the Bayesian estimations based on the asymmetric loss function to the traditional technique or the symmetric loss function-based Bayesian estimations, the analysis demonstrates that asymmetric loss function-based Bayesian estimations are preferred. Finally, two data sets, representing vinyl chloride and repairable mechanical equipment items, have been investigated to support the approaches proposed and show the superiority of the proposed model compared to the other fourteen lifetime models.

show abstract

Inference for Unit Inverse Weibull Distribution Under Block Progressive Type-II Censoring

Singh,

Tripathi,

Lodhi

et al. 2024

J Stat Theory Pract

View full text Add to dashboard Cite

Product of spacing estimation of entropy for inverse Weibull distribution under progressive type-II censored data with applications

Cited by 8 publications

References 27 publications

On Entropy Estimation of Inverse Weibull Distribution under Improved Adaptive Progressively Type-II Censoring with Applications

On Entropy Estimation of Inverse Weibull Distribution under Improved Adaptive Progressively Type-II Censoring with Applications

Computational Analysis of Novel Extended Lindley Progressively Censored Data

Inference for Unit Inverse Weibull Distribution Under Block Progressive Type-II Censoring

Contact Info

Product

Resources

About