Bayesian analysis of Weibull distribution based on progressive type-II censored competing risks data with binomial removals

Chacko, Manoj; Mohan, Rakhi

doi:10.1007/s00180-018-0847-2

Cited by 36 publications

(13 citation statements)

References 17 publications

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“…Using the concept of informative priors, the hyperparameters are selected in such a manner that the expectation of each unknown parameter's prior distribution is identical to the actual value. Numerous scientists, notably Chacko and Mohan [ 27 ], have employed this strategy to great effect. This section investigates the use of Bayesian estimating to get estimates of the TIHLBW model parameters in order to realize those estimations.…”

Section: Bayesian Estimationmentioning

confidence: 99%

Novel Type I Half Logistic Burr-Weibull Distribution: Application to COVID-19 Data

Alshanbari

Odhah

Almetwally

et al. 2022

Computational and Mathematical Methods in Medicine

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In this work, we presented the type I half logistic Burr-Weibull distribution, which is a unique continuous distribution. It offers several superior benefits in fitting various sorts of data. Estimates of the model parameters based on classical and nonclassical approaches are offered. Also, the Bayesian estimates of the model parameters were examined. The Bayesian estimate method employs the Monte Carlo Markov chain approach for the posterior function since the posterior function came from an uncertain distribution. The use of Monte Carlo simulation is to assess the parameters. We established the superiority of the proposed distribution by utilising real COVID-19 data from varied countries such as Saudi Arabia and Italy to highlight the relevance and flexibility of the provided technique. We proved our superiority using both real data.

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Section: Bayesian Estimationmentioning

confidence: 99%

Novel Type I Half Logistic Burr-Weibull Distribution: Application to COVID-19 Data

Alshanbari

Odhah

Almetwally

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Assuming that any single unit removed from the study is independent of other units but has the same probability p, the number of removed units will follow a binomial distribution. Reference [11] assumed that the censored units are random at every stage and follow a binomial distribution and considered the analysis of competing risks data which obey the Weibull distribution under the progressive type-II censoring model. How to obtain progressive type-II censored data with binomial removals is described below.…”

Section: Progressive Type-ii Censored Data With Binomial Removalsmentioning

confidence: 99%

Parameter Estimation of Lindley Distribution Based on Progressive Type-II Censored Competing Risks Data with Binomial Removals

Nie

Gui

2019

Mathematics

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The competing risk model based on Lindley distribution is discussed under the progressive type-II censored sample data with binomial removals. The maximum likelihood estimation of the unknown parameters of the distribution is established. Using the Lindley approximation method, we also obtain the Bayesian estimation of the unknown parameters of the distribution under different loss functions. The performance of different estimates is studied in this article. A real practical dataset is analyzed for illustration.

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“…proposed an extended Weibull distribution having various shapes of hazard rates as constant, decreasing, increasing, unimodal and bathtub shape. Motivated by the applicability of Weibull distribution and a more realistic scenario in the study of partially observed competing risks data (see, for example, Ashour and Nassar 21 and Chacko and Mohan 22 ), this paper aims to derive statistical inferences under UHCS when the latent failure times follow Weibull distributions with partially observed failure causes. It is assumed that the latent failure times follow Weibull distributions with different scale parameters

{\lambda }_{j}&gt;0

, for

j=1,\nobreakspace 2

and a common shape parameter

\alpha &gt;0

.…”

Section: Introductionmentioning

confidence: 99%

Bayesian and non‐Bayesian inference of Weibull lifetime model based on partially observed competing risks data under unified hybrid censoring scheme

Dutta

Kayal

2022

Quality & Reliability Eng

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In this paper, a competing risk model is analyzed based on unified hybrid censoring scheme (UHCS). It is assumed that the latent failure times follow Weibull distributions with a common shape and different scale parameters. The maximum likelihood estimates (MLEs) and approximate confidence intervals (ACIs) of the distributional parameters are obtained. Sufficient conditions under which the MLEs exist (uniquely) have been studied. Further, the Bayes estimates are obtained with respect to symmetric and asymmetric loss functions. The noninformative and informative prior distributions are considered for computing the approximate Bayes estimates. Furthermore, the highest posterior density (HPD) credible intervals have been obtained by using Markov Chain Monte Carlo (MCMC) method with the Gibbs sampling technique. The coverage probabilities for each confidence interval are computed. Then, hypothesis testing has been implemented using likelihood ratio statistic. A Monte Carlo simulation study is carried out to compare the performance of the proposed estimates. Finally, two real data sets are provided to illustrate the estimates.

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Bayesian analysis of Weibull distribution based on progressive type-II censored competing risks data with binomial removals

Cited by 36 publications

References 17 publications

Novel Type I Half Logistic Burr-Weibull Distribution: Application to COVID-19 Data

Novel Type I Half Logistic Burr-Weibull Distribution: Application to COVID-19 Data

Parameter Estimation of Lindley Distribution Based on Progressive Type-II Censored Competing Risks Data with Binomial Removals

Bayesian and non‐Bayesian inference of Weibull lifetime model based on partially observed competing risks data under unified hybrid censoring scheme

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