Inference for Weibull distribution based on progressively Type-II hybrid censored data

Mokhtari, Elham Bayat; Rad, A. Habibi; Yousefzadeh, Fatemeh

doi:10.1016/j.jspi.2011.03.007

Cited by 41 publications

(10 citation statements)

References 22 publications

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“…This dataset was analyzed by different authors using various models [ 43 , 44 ]. In [ 19 ], the authors found that the MED model with two components

provided a good fit to the given failure data.…”

Section: Data Analysismentioning

confidence: 99%

Evidential Estimation of an Uncertain Mixed Exponential Distribution under Progressive Censoring

Zhou

Shi

2020

Entropy

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In this paper, the evidential estimation method for the parameters of the mixed exponential distribution is considered when a sample is obtained from Type-II progressively censored data. Different from the traditional statistical inference methods for censored data from mixture models, here we consider a very general form where there is some uncertain information about the sub-class labels of units. The partially specified label information, as well as the censored data are represented in a united frame by mass functions within the theory of belief functions. Following that, the evidential likelihood function is derived based on the completely observed failures and the uncertain information included in the data. Then, the optimization method using the evidential expectation maximization algorithm (E2M) is introduced. A general form of the maximal likelihood estimates (MLEs) in the sense of the evidential likelihood, named maximal evidential likelihood estimates (MELEs), can be obtained. Finally, some Monte Carlo simulations are conducted. The results show that the proposed estimation method can incorporate more information than traditional EM algorithms, and this confirms the interest in using uncertain labels for the censored data from finite mixture models.

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“…This dataset was analyzed by different authors using various models [ 43 , 44 ]. In [ 19 ], the authors found that the MED model with two components

provided a good fit to the given failure data.…”

Section: Data Analysismentioning

confidence: 99%

Evidential Estimation of an Uncertain Mixed Exponential Distribution under Progressive Censoring

Zhou

Shi

2020

Entropy

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“…A numerical comparison is performed to compare the performance of the proposed methods and two illustrative examples are discussed. One may also refer to Mokhtari et al (2011), Balakrishnan and Kundu (2013), Kayal et al (2017) for some more interesting applications of Type II progressive hybrid censoring in life testing experiments.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation and Prediction for the Generalized Half Normal Distribution under Progressive Hybrid Censoring

Sultana

Tripathi

Rastogi

2019

JIRSS

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In this paper, the problem of estimating unknown parameters of a generalized half-normal distribution is considered under Type II progressive hybrid censoring which is a combination of Type II progressive and hybrid censoring schemes. We obtain maximum likelihood estimators of parameters and also construct asymptotic intervals using the observed Fisher information matrix. Further Bayes estimates are computed under the squared error loss function by applying different approximation methods. We also obtain prediction estimates and prediction intervals of censored observations. The performance of different methods is compared using Monte Carlo simulations and a real data set is analyzed for illustrative purposes.

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“…It is easy to see that this censoring scheme can guar antee at least pre-fixed number of failures observed, result ing in lower variability in the reliability evaluation. And that is the primary reason why the model has attracted great attention in the field of reliability [14][15][16][17][18][19][20][21][22][23]. Among these papers, Park et al [15] obtained explicit expression of the Fisher information under the Type-II hybrid censor ing scheme.…”

Section: Introductionmentioning

confidence: 99%

“…Conditional on (( s ), the PDF of e jh Luj�lgx ( ' ) + 1 h Z mE(C,) 6, EQi 1 =0 ' 1 d P ( (( d ) I D = d) L L L uj �igX(d)(18) mE(( d) 0, EQi 1 =0 .…”

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confidence: 99%

Exact inference for two exponential populations with competing risks data

Mao

Shi

Wang

2014

J. of Syst. Eng. Electron.

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In a reliability comparative test, the joint censoring model is usually adopted to evaluate the performances of units with the same facility. However, most researchers ignore the pos sibility that there is more than one factor for the failure when a test unit fails. To solve this problem, we consider a joint Type-II hybrid censoring model for the analysis of exponential competing failure data. Based on the maximum likelihood theory, we compute the maximum likelihood estimators (MLEs) of parameters and then obtain the condition ensuring MLEs existence for every unknown parameter. Then we derive the conditional exact distributions and corresponding moment properties for parameters by the moment generating function (MGF). A Monte-Carlo simulation is conducted to compare the performances of different ways. And finally, we conduct a numerical example to illustrate the proposed method.

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Inference for Weibull distribution based on progressively Type-II hybrid censored data

Cited by 41 publications

References 22 publications

Evidential Estimation of an Uncertain Mixed Exponential Distribution under Progressive Censoring

Evidential Estimation of an Uncertain Mixed Exponential Distribution under Progressive Censoring

Parameter Estimation and Prediction for the Generalized Half Normal Distribution under Progressive Hybrid Censoring

Exact inference for two exponential populations with competing risks data

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