Exact inference for the two-parameter exponential distribution under Type-II hybrid censoring

Ganguly, Ayon; Mitra, Sharmistha; Samanta, Debasis; Kundu, Debasis

doi:10.1016/j.jspi.2011.08.001

Cited by 27 publications

(15 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the current decade, Schenk, Burkschat, Cramer and Kamps [19] studied the Bayesian prediction and estimation of consecutive order statistics for Type-II censored samples taken from one-and two-parameter exponential distributions. Ganguly, Mitra, Samanta and Kundu [7] derived the exact distribution of the hybrid Type-II censoring scheme, where the two-parameter exponential distribution is used to model lifetimes. Based on generalized pivot variable, Baklizi [2] introduced bootstrap and Bayesian intervals estimation of stress-strength reliability for the two-parameter exponential distribution.…”

Section: Introductionmentioning

confidence: 99%

Exact simultaneous location-scale tests for two shifted exponential samples

Mukherjee¹,

Chong²,

Marozzi³

2020

Kybernetika

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Exact simultaneous location-scale tests for two shifted exponential samples

Mukherjee¹,

Chong²,

Marozzi³

2020

Kybernetika

View full text Add to dashboard Cite

show abstract

“…Singh and Kumar (2007) provided the Bayes estimators of the exponential parameter under the multiply Type-II censoring scheme. Ganguly et al (2012) studied the exact inference for the two-parameter exponential distribution under the Type-II hybrid censoring scheme. Childs et al (2012) studied the exact distribution of the maximum likelihood estimators (MLEs) of the parameters and of the quantiles of two-parameter exponential distribution under the hybrid censoring scheme.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation for the exponential distribution based on generalized multiply Type-II hybrid censoring

Jeon

Kang

2020

CSAM

View full text Add to dashboard Cite

The multiply Type-II hybrid censoring scheme is disadvantaged by an experiment time that is too long. To overcome this limitation, we propose a generalized multiply Type-II hybrid censoring scheme. Some estimators of the scale parameter of the exponential distribution are derived under a generalized multiply Type-II hybrid censoring scheme. First, the maximum likelihood estimator of the scale parameter of the exponential distribution is obtained under the proposed censoring scheme. Second, we obtain the Bayes estimators under different loss functions with a noninformative prior and an informative prior. We approximate the Bayes estimators by Lindleys approximation and the Tierney-Kadane method since the posterior distributions obtained by the two priors are complicated. In addition, the Bayes estimators are obtained by using the Markov Chain Monte Carlo samples. Finally, all proposed estimators are compared in the sense of the mean squared error through the Monte Carlo simulation and applied to real data.

show abstract

“…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%

Exact inference for two exponential populations with competing risks data

Mao

Shi

Wang

2014

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

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.

show abstract

Exact inference for the two-parameter exponential distribution under Type-II hybrid censoring

Cited by 27 publications

References 8 publications

Exact simultaneous location-scale tests for two shifted exponential samples

Exact simultaneous location-scale tests for two shifted exponential samples

Bayesian estimation for the exponential distribution based on generalized multiply Type-II hybrid censoring

Exact inference for two exponential populations with competing risks data

Contact Info

Product

Resources

About