On progressively censored generalized exponential distribution

Pradhan, Biswabrata; Kundu, Debasis

doi:10.1007/s11749-008-0110-1

Cited by 139 publications

(55 citation statements)

References 23 publications

(27 reference statements)

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“…Madi and Raqab (2009) considered the problem of Bayesian esti-mation of the parameters as well as Bayesian prediction of the unobserved failure times censored in multiple stages in the progressively censored sample from the GE distribution (Bayesian one-sample prediction) using the Gibbs and Metropolis samplers. Also, see Kundu and Gupta (2008); Mitra and Kundu (2008); Kundu and Pradhan (2009a,b); Pradhan and Kundu (2009) and the references therein. Recently, Kim and Han (2015) considered the problem of maximum likelihood and Bayes estimation of the GE parameters as well as predicting an independent future order statistics from the GE distribution based on progressively first failure censored samples.…”

Section: The Ge Distributionmentioning

confidence: 99%

Bayesian Two-sample Prediction with Progressively Censored Data for Generalized Exponential Distribution Under Symmetric and Asymmetric Loss Functions

Ghafouri¹,

Rad²,

Doostparast³

2016

JSRI

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Abstract. Statistical prediction analysis plays an important role in a wide range of fields. Examples include engineering systems, design of experiments, etc. In this paper, based on progressively Type-II right censored data, Bayesian two-sample point and interval predictors are developed under both informative and non-informative priors. By assuming a generalized exponential model, prediction bounds as well as Bayes point predictors are obtained under the squared error loss (SEL) and the Linear-Exponential (LINEX) loss functions for the order statistic in a future progressively Type-II censored sample with an arbitrary progressive censoring scheme. The derived results may be used for prediction of total time on test in lifetime experiments. In addition to numerical method, Gibbs sampling procedure (as Markov Chain Monte Carlo method) are used to assess approximate prediction bounds and Bayes point predictors under the SEL and LINEX loss functions. The performance of the proposed prediction procedures are also demonstrated via a Monte Carlo simulation study and an illustrative example, for each method.

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Section: The Ge Distributionmentioning

confidence: 99%

Bayesian Two-sample Prediction with Progressively Censored Data for Generalized Exponential Distribution Under Symmetric and Asymmetric Loss Functions

Ghafouri¹,

Rad²,

Doostparast³

2016

JSRI

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“…Type-II censoring: If the experiment is terminated at the rth failure, that is at time X r:n , we obtain type-II censored sample, here r is …xed, while X r:n the duration of the experiment is random. Many articles in this literature have discussed inferential method under type-I and type-II censoring for various parametric families of distributions, for more details, see for example, [1,3,5,7,9,11].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of the Scale Parameter of the Marshall-Olkin Exponential Distribution under Progressively Type-II Censored Samples

Salah¹

2018

JSTA

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Abstract:This paper studies the Bayes estimator, the maximum likelihood estimator and the approximate likelihood estimator of the scale parameter for the Marshall-Olkin exponential distribution under the progressive type-II censored sample. All the estimators, Bayes estimator, maximum likelihood estimator and approximate likelihood estimator are presented and derived in simple forms. It observed that the Bayes estimator and the maximum likelihood estimator can not be solved analytically, hence it is solved numerically. Finally the comparison method is presented in order to compare the performance between these estimators.

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“…(2003) ] and [Balakrishnan and Asgharzadeh (2005)] have discussed inference procedures based on MLEs and approximate MLEs for logistic, normal and half-logistic distributions, respectively. [Pradhan and Kundu (2009)] have obtained maximum likelihood estimates of the parameters of generalized exponential distribution. [Kim and Han (2009)] have considered estimation of the scale parameter of the Rayleigh distribution under general progressive censoring.…”

Section: Introductionmentioning

confidence: 99%

Estimating the parameters of lifetime distributions under progressively Type-II censoring from fuzzy data

Khoolenjani

Chatrabgoun

2016

Journal of Applied Mathematics, Statistics and Informatics

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The problem of estimating lifetime distribution parameters under progressively Type-II censoring originated in the context of reliability. But traditionally it is assumed that the available data from this censoring scheme are performed in exact numbers. However, some collected lifetime data might be imprecise and are represented in the form of fuzzy numbers. Thus, it is necessary to generalize classical statistical estimation methods for real numbers to fuzzy numbers. This paper deals with the estimation of lifetime distribution parameters under progressively Type-II censoring scheme when the lifetime observations are reported by means of fuzzy numbers. A new method is proposed to determine the maximum likelihood estimates of the parameters of interest. The methodology is illustrated with two popular models in lifetime analysis, the Rayleigh and Lognormal lifetime distributions.

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On progressively censored generalized exponential distribution

Abstract: Maximum likelihood estimators, EM algorithm, Fisher information matrix, Asymptotic distribution, Optimal censoring scheme, 62N05, 62F10,

Cited by 139 publications

References 23 publications

Bayesian Two-sample Prediction with Progressively Censored Data for Generalized Exponential Distribution Under Symmetric and Asymmetric Loss Functions

Bayesian Two-sample Prediction with Progressively Censored Data for Generalized Exponential Distribution Under Symmetric and Asymmetric Loss Functions

Bayesian Estimation of the Scale Parameter of the Marshall-Olkin Exponential Distribution under Progressively Type-II Censored Samples

Estimating the parameters of lifetime distributions under progressively Type-II censoring from fuzzy data

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