Bayesian survival analysis for adaptive Type-II progressive hybrid censored Hjorth data

Elshahhat, Ahmed; Nassar, Mazen

doi:10.1007/s00180-021-01065-8

Cited by 38 publications

(24 citation statements)

References 30 publications

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“…Extensive evaluations are performed using

\mathcal {R}

software via three recommended packages; namely: ‘CODA’ package proposed by reference 22 to carry out the computations of Bayes estimates via MCMC techniques; ‘maxLik’ package proposed by reference 15 and ‘GoFKernel’ package proposed by reference 23 considered to simulate a sequence of random variates by inverting (). These useful packages were recently recommended by 24 …”

Section: Numerical Analysismentioning

confidence: 99%

Analysis of Wilson‐Hilferty distribution under progressive Type‐II censoring

Dey¹,

Elshahhat

2022

Quality & Reliability Eng

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A new re-parameterize form of the Wilson-Hilferty distribution for data modelling with increasing, decreasing and bathtub shape hazard rates has been considered. This paper takes into account the estimation for the unknown model parameters, reliability function and hazard function based on two frequentist methods and Bayesian method of estimation using Type-II progressively censored data. In frequentist method, besides conventional likelihood based estimation, another competitive method, known as maximum product of spacing (MPS) method is proposed to estimate the model parameters, reliability function and hazard function as an alternative approach to the common likelihood method. In Bayesian paradigm, we have also considered the MPS function as an alternative to the traditional likelihood function and both are also discussed under the Bayesian set up for unknown parameters, reliability function and hazard function. Moreover, for all considered unknown quantities, the approximate confidence intervals under the proposed frequentist approaches as well as the Bayes credible intervals are constructed. Extensive Monte-Carlo simulation studies are conducted to evaluate the performance of the proposed estimates with respect to various criteria quantities. Furthermore, we discuss an optimal progressive censoring plan among different competing censoring plans using three optimality criteria. Finally, to show the applicability of the proposed methodologies in a real-life scenario, an engineering dataset is analysed.

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“…Extensive evaluations are performed using

\mathcal {R}

Section: Numerical Analysismentioning

confidence: 99%

Analysis of Wilson‐Hilferty distribution under progressive Type‐II censoring

Dey¹,

Elshahhat

2022

Quality & Reliability Eng

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“…. , N , the approximate BEs with corresponding mean squared-errors (MSEs) of a parametric function ϕ(α, β, λ), or any function of them such as R(t) and h(t) based on SEL (10), SLEL (11), WSEL (12) and MSEL (13) functions, are given respectively by…”

Section: Bayes Proceduresmentioning

confidence: 99%

Bayesian Life Analysis of Generalized Chen's Population Under Progressive Censoring

Elshahhat¹,

Rastogi

2022

Pak.j.stat.oper.res.

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Chen's model with bathtub shape provides an appropriate conceptual for the hazard rate of various industrial products and clinical cases. This article deals with the problem of estimating the model parameters, reliability and hazard functions of a three-parameter Chen distribution based on progressively Type-II censored sample have been obtained. Based on the s-normal approximation to the asymptotic distribution of the maximum likelihood estimates and log-transformed maximum likelihood estimates, the approximate confidence intervals for the unknown parameters, and any function of them, are constructed. Using independent gamma conjugate priors, the Bayes estimators of the unknown parameters and reliability characteristics are derived under different versions of a symmetric squared error loss functions. However, the Bayes estimators are obtained in a complex form, so we have been used Metropolis-Hastings sampler procedure to carry out the Bayes estimates and also to construct the corresponding credible intervals. To assess the performance of the proposed estimators, numerical results using Monte Carlo simulation study were reported. To determine the optimum censoring scheme among different competing censoring plans, some optimality criteria have been considered. A practical example using real-life data set, representing the survival times of head and neck cancer patients, is discussed to demonstrate how the applicability of the proposed methods in real phenomenon.

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“…Vital statistics about the parameters are also computed from the 25,000 generated sample values. All assessments are carried out by using R statistical computer language and two useful statistical packages suggested by Elshahhat and Nassar [18], namely the "CODA" package, which is used to carry out the computations of the MCMC suggestion made by Plummer et al [19], and the "maxLik" package, which is used to carry out the computations of the N-R method of maximization proposed by Henningsen and Toomet [20].…”

Section: Head-neck Cancer Datamentioning

confidence: 99%

Statistical Analysis Based on Progressive Type‐I Censored Scheme from Alpha Power Exponential Distribution with Engineering and Medical Applications

Abo-Kasem

Sallam

Aljohani

et al. 2022

Journal of Mathematics

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Using a progressive Type-I censoring technique, this article will explore how to estimate unknown parameters of the alpha power exponential distribution (APED) (Type-I PCS). The squared error loss function and the LINEX loss function are used to get the maximum likelihood estimate as well as the Bayesian estimation of the unknown parameters, respectively. It was our intention to use the Markov chain Monte Carlo method in conjunction with the Bayes estimation strategy. We are able to calculate the approximately accurate confidence intervals for the parameters whose values are unknown. In addition to this, we discussed the estimation challenges of reliability and the hazard rate function of the APED while using Type-I PCS, as well as the approximate confidence intervals that relate to these problems. In the last step, the theoretical findings that were acquired are evaluated and compared with the distributions of all of its rivals by making use of two actual datasets that represent the disciplines of engineering and medicine.

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Bayesian survival analysis for adaptive Type-II progressive hybrid censored Hjorth data

Cited by 38 publications

References 30 publications

Analysis of Wilson‐Hilferty distribution under progressive Type‐II censoring

Analysis of Wilson‐Hilferty distribution under progressive Type‐II censoring

Bayesian Life Analysis of Generalized Chen's Population Under Progressive Censoring

Statistical Analysis Based on Progressive Type‐I Censored Scheme from Alpha Power Exponential Distribution with Engineering and Medical Applications

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