Exact Likelihood Inference for an Exponential Parameter under Generalized Adaptive Progressive Hybrid Censoring

In this paper, we propose a new type censoring scheme named a generalized adaptive progressive hybrid censoring scheme (GenAdPrHyCS). In this new type censoring scheme, the experiment is assured to stop at a pre-assigned time. This censoring scheme is designed to correct the drawbacks in the AdPrHyCS. Furthermore, we discuss inference for one parameter exponential distribution (ExD) under GenAdPrHyCS. We derive the moment generating function of the maximum likelihood estimator (MLE) of scale parameter of ExD and the resulting lower confidence bound under GenAdPrHyCS.

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Exact likelihood inference for an exponential parameter under generalized progressive hybrid censoring scheme

Statistical Methodology ◽

10.1016/j.stamet.2014.09.002 ◽

2015 ◽

Vol 23 ◽

pp. 18-34 ◽

Cited By ~ 26

Author(s):

Youngseuk Cho ◽

Hokeun Sun ◽

Kyeongjun Lee

Keyword(s):

Likelihood Inference ◽

Exponential Parameter ◽

Hybrid Censoring ◽

Censoring Scheme

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Exact Inference for an Exponential Parameter under Generalized Adaptive Progressive Hybrid Censored Competing Risks Data

Symmetry ◽

10.3390/sym12122005 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2005

Author(s):

Youngseuk Cho ◽

Kyeongjun Lee

Keyword(s):

Risk Model ◽

Moment Generating Function ◽

Conditional Moment ◽

Exponential Parameter ◽

Exact Inference ◽

Scale Parameters ◽

Lower Confidence Bound ◽

Competing Risks Data ◽

Conditional Moment Generating Function ◽

Censoring Scheme

It is known that the lifetimes of items may not be recorded exactly. In addition, it is known that more than one risk factor (RisF) may be present at the same time. In this paper, we discuss exact likelihood inference for competing risk model (CoRiM) with generalized adaptive progressive hybrid censored exponential data. We derive the conditional moment generating function (ConMGF) of the maximum likelihood estimators of scale parameters of exponential distribution (ExpD) and the resulting lower confidence bound under generalized adaptive progressive hybrid censoring scheme (GeAdPHCS). From the example data, it can be seen that the PDF of MLE is almost symmetrical.

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Bayesian Estimation of the Exponential Parameter under a Multiply Type-II Censoring Scheme

Austrian Journal of Statistics ◽

10.17713/ajs.v36i3.334 ◽

2016 ◽

Vol 36 (3) ◽

Cited By ~ 5

Author(s):

Umesh Singh ◽

Anil Kumar

Keyword(s):

Monte Carlo ◽

Loss Function ◽

Scale Parameter ◽

Type Ii ◽

Bayes Estimators ◽

Conjugate Prior ◽

Exponential Parameter ◽

Type Ii Censoring ◽

Error Loss ◽

Censoring Scheme

This paper provides the estimation of the scale parameter of the exponential distribution under multiply type-II censoring. Using generalized non-informative prior and natural conjugate prior, Bayes estimator and approximate Bayes estimators of the scale parameter have been obtained under square error loss function. The proposed Bayes estimators and approximate Bayes estimators are compared with the estimators proposed by Singh et al. (2005) and Balasubramanian and Balakrishnan (1992) on the basis of theirsimulated risks under square error loss function of 1000 randomly generated Monte Carlo samples.

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Estimation of the Rayleigh Distribution under Unified Hybrid Censoring

Austrian Journal of Statistics ◽

10.17713/ajs.v50i1.990 ◽

2021 ◽

Vol 50 (1) ◽

pp. 59-73

Author(s):

Young Eun Jeon ◽

Suk-Bok Kang

Keyword(s):

Posterior Distribution ◽

Mean Squared Error ◽

Scale Parameter ◽

Interval Estimation ◽

Credible Interval ◽

Rayleigh Distribution ◽

Bayes Estimator ◽

Hybrid Censoring ◽

The Mean ◽

Censoring Scheme

We derive some estimators of the scale parameter of the Rayleigh distribution under the unified hybrid censoring scheme. We also derive some estimators of the reliability function and the entropy of the Rayleigh distribution. First, we obtain the maximum likelihood estimator of the scale parameter. Second, we obtain the Bayes estimator using the mean of the posterior distribution. Lastly, we obtain the Bayes estimator using the mode of the posterior distribution. We also derive the interval estimation (confidence interval, credible interval, and HPD credible interval) for the scale parameter under the unified hybrid censoring scheme. We compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. Coverage probability and average lengths of 95 % and 90% intervals are obtained.

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