2020
DOI: 10.1002/qre.2791
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Inference for two‐parameter Rayleigh competing risks data under generalized progressive hybrid censoring

Abstract: In this paper, a competing risks model based on the generalized progressive hybrid censored two‐parameter Rayleigh distributions is studied under the assumption that the lifetime distributions of failure causes are identically distributed with same location and different scale parameters. We obtain maximum likelihood estimates of unknown parameters with associated existence uniqueness. The approximate confidence intervals are constructed using the asymptotic distribution of maximum likelihood estimates via the… Show more

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Cited by 13 publications
(9 citation statements)
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References 53 publications
(70 reference statements)
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“…Theorem 2. Suppose that observation (2) comes from Weibull population (5); the MLE of α obtained from (10) uniquely exists which can be derived from the following equation:…”
Section: Maximum Likelihood Estimationmentioning
confidence: 99%
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“…Theorem 2. Suppose that observation (2) comes from Weibull population (5); the MLE of α obtained from (10) uniquely exists which can be derived from the following equation:…”
Section: Maximum Likelihood Estimationmentioning
confidence: 99%
“…In addition, the Bayes credible intervals under NIP also have better performance than ACIs in terms of interval length. Besides, to illustrate eorem 2 and Lemma 1, the curves of the profile log-likelihood function (10) and the logarithmic function of posterior marginal density (21) with respect to NIP are plotted in Figure 4 under the censored failure data. It is observed that the profile log-likelihood function of α has a unique maximum point and the posterior marginal density function π(α|data) is log-concave.…”
Section: Real-life Illustrationmentioning
confidence: 99%
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“…On the basis of GPHCS, Ref. [ 14 ] investigated the two-parameter Rayleigh competing risk data adopting the maximum likelihood estimation and Gibbs sampling technique was employed to approximate the associated Bayes estimates.…”
Section: Introductionmentioning
confidence: 99%