Inference Based on Type-II Hybrid Censored Data From a Weibull Distribution

Banerjee, Arunava; Kundu, Debasis

doi:10.1109/tr.2008.916890

Cited by 105 publications

(43 citation statements)

References 22 publications

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“…Recently Kundu [16] and Banerjee and Kundu [3] also suggested similar approximations to compute the approximate MLEs of Weibull parameters when the data are hybrid censored.…”

Section: Approximate Mlementioning

confidence: 99%

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Parameter estimation of the hybrid censored log-normal distribution

Dube

Pradhan

Kundu

2011

Journal of Statistical Computation and Simulation

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The two most common censoring schemes used in life testing experiments are Type-I and Type-II censoring schemes. Hybrid censoring scheme is mixture of Type-I and Type-II censoring scheme. In this work we consider the estimation of parameters of log-normal distribution based on hybrid censored data. The parameters are estimated by the maximum likelihood method. It is observed that the maximum likelihood estimates can not be obtained in closed form. We obtain the maximum likelihood estimates of the unknown parameters using EM algorithm. We also propose approximate maximum likelihood estimates and these can be used as initial estimates for any iterative procedure. The Fisher information matrix has been obtained and it can be used for constructing asymptotic confidence intervals. The method of obtaining optimum censoring scheme is discussed. One data set is analyzed for illustrative purposes.

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“…Recently Kundu [16] and Banerjee and Kundu [3] also suggested similar approximations to compute the approximate MLEs of Weibull parameters when the data are hybrid censored.…”

Section: Approximate Mlementioning

confidence: 99%

“…Comparisons and criticisms of the different methods can be found in Gupta and Kundu [12]. For some of the relevant references on hybrid censoring and related topics the readers are referred to Ebrahimi [8,9], Jeong et al [14], Childs et al [5], Kundu [16], Banerjee and Kundu [3], Kundu and Pradhan [18] and the references cited there.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of the hybrid censored log-normal distribution

Dube

Pradhan

Kundu

2011

Journal of Statistical Computation and Simulation

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“…Inference, sampling design and generalization based on progressively censored samples were studied by many authors, see among others, Balasooriya et al [3], Ng et al [4], Balakrishnan et al [5], Fernandez [6], Soliman [7], Asgharzadeh [8], Ku and Kaya [9], Wu et al [10], Banerjee and Kundu [11] and Raqab et al [12]. However, a little work is introduced in the Bayesian context.…”

Section: Introductionmentioning

confidence: 99%

Inferences using type-II progressively censored data with binomial removals

et al. 2015

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This paper considers the estimation problem for Burr type-X model, when the lifetimes are collected under type-II progressive censoring with random removals, where the number of units removed at each failure time follows a binomial distribution. The methods of maximum likelihood as well as the Bayes procedure to derive both point and interval estimates of the parameters are used. The expected test time to complete the censoring test is computed and analyzed for different censoring schemes. The effect of the binomial distribution parameter p on the expected test time under progressive censoring and the relative expected test time over the complete sample are investigated. Monte Carlo simulations are performed to compare and evaluate the performance of different methods. Furthermore, an example with a real data set is presented for illustrative purposes.

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“…It is clear that the test have at least R failure items in Type-II hybrid censoring scheme where as in Type-I hybrid censoring scheme, the test can never be reached beyond the time T 0 . Many authors have discussed the estimation procedures of the parameters of the various life-time models under Type-I hybrid, see for example [21,16,24,12] and Type-II hybrid [6,11,23,2] censoring schemes. [19] and [7] have derived the density of the rth future sample by using the concept of the Bayesian predictive posterior from Type-II censored sample follow the exponential distribution.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Prediction of Future Observations from Inverse Weibull Distribution Based on Type-II Hybrid Censored Sample

Singh

Sharma

2013

International Journal of Advanced Statistics and Probability

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In this paper, we have discussed the Bayesian procedure for the prediction of the future samples from inverse Weibull (IW) distribution under Type-II hybrid censoring scheme. Bayes estimators along with the corresponding highest posterior density (HPD) credible intervals have also been constructed for the parameters of IW distribution. The performance of the Bayes estimators of the model parameters has been compared with the maximum likelihood estimators through Monte Carlo Markov chain (MCMC) techniques. Finally, a real data set has been analysed to illustrate the discussed methodology.

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Inference Based on Type-II Hybrid Censored Data From a Weibull Distribution

Cited by 105 publications

References 22 publications

Parameter estimation of the hybrid censored log-normal distribution

Parameter estimation of the hybrid censored log-normal distribution

Inferences using type-II progressively censored data with binomial removals

Bayesian Prediction of Future Observations from Inverse Weibull Distribution Based on Type-II Hybrid Censored Sample

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