Inference for partially observed competing risks model for Kumaraswamy distribution under generalized progressive hybrid censoring

Mahto, Amulya Kumar; Lodhi, Chandrakant; Tripathi, Yogesh Mani; Wang, Liang

doi:10.1080/02664763.2021.1889999

Cited by 17 publications

(9 citation statements)

References 39 publications

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“…In recent literature, several authors have considered this scheme using some lifetime distributions, among others see for example. [6][7][8][9][10][11][12][13][14][15] A new two-parameter extended exponential distribution was introduced elsewhere 16 and was recently named NHD as an abbreviation of the author's name. Suppose that the lifetime 𝑋 of a testing unit follows two-parameter NHD(𝛽, 𝜆), then the probability density function (PDF) 𝑓(⋅) and cumulative distribution function (CDF) 𝐹(⋅), survival function (SF) 𝑆(⋅) and hazard rate function (HRF) 𝐻(⋅), for given mission time 𝑡, are given respectively by…”

Section: Introductionmentioning

confidence: 99%

“…Putting 𝛽 = 1 in (3), the exponential distribution is introduced as a special case. 16 showed that the PDF (3) can be decreasing and unimodal shapes as well as the failure rate function have an increasing, decreasing or constant shape. In fact, from (6), the HRF has a similar behavior for some well-known lifetime models such as gamma, Weibull and generalized-exponential distributions.…”

Section: Introductionmentioning

confidence: 99%

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Statistical reliability analysis of electronic devices using generalized progressively hybrid censoring plan

Elshahhat

Azm

2022

Quality & Reliability Eng

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Generalized progressive hybrid censoring plan proposed to overcome the limitation of the progressive hybrid censoring scheme is that it cannot be applied when very few failures may occur before pre-specified terminal time 𝑇. In this paper, the estimating problems of the model parameters, reliability and hazard rate functions of Nadarajah-Haghighi distribution when a sample is available from generalized progressive hybrid censoring have been considered. The maximum likelihood and Bayes estimators have been obtained for any function of the model parameters. Approximate confidence intervals for the unknown parameters and any function of them are constructed. Using independent gamma informative priors, the Bayes estimators of the unknown parameters are derived under the squared-error loss function. Two approximation techniques, namely: Lindley approximation method and Metropolis Hastings algorithm have been used to carry out the Bayes estimates and also to construct the associate highest posterior density credible intervals. The performance of the proposed methods are evaluated through a Monte Carlo simulation study. To select the optimum censoring scheme among different competing censoring plans, different optimality criteria have been considered. A real-life dataset, representing the failure times of electronic devices, is analyzed to demonstrate how the applicability of the proposed methodologies in real phenomenon.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical reliability analysis of electronic devices using generalized progressively hybrid censoring plan

Elshahhat

Azm

2022

Quality & Reliability Eng

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“…In this paper, competing risks data are analyzed with respect to the latent failure time model. In recent past, competing risks models have been considered by several authors, see for instance, Kundu and Basu 3 , Sarhan et al 4 , Wang 5 , Mahto et al 6 , Ren and Gui 7 , Wang et al, 8 and Wang et al 9 .…”

Section: Introductionmentioning

confidence: 99%

Bayesian and non‐Bayesian inference of Weibull lifetime model based on partially observed competing risks data under unified hybrid censoring scheme

Dutta

Kayal

2022

Quality & Reliability Eng

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In this paper, a competing risk model is analyzed based on unified hybrid censoring scheme (UHCS). It is assumed that the latent failure times follow Weibull distributions with a common shape and different scale parameters. The maximum likelihood estimates (MLEs) and approximate confidence intervals (ACIs) of the distributional parameters are obtained. Sufficient conditions under which the MLEs exist (uniquely) have been studied. Further, the Bayes estimates are obtained with respect to symmetric and asymmetric loss functions. The noninformative and informative prior distributions are considered for computing the approximate Bayes estimates. Furthermore, the highest posterior density (HPD) credible intervals have been obtained by using Markov Chain Monte Carlo (MCMC) method with the Gibbs sampling technique. The coverage probabilities for each confidence interval are computed. Then, hypothesis testing has been implemented using likelihood ratio statistic. A Monte Carlo simulation study is carried out to compare the performance of the proposed estimates. Finally, two real data sets are provided to illustrate the estimates.

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“…Of course, in absence of such an information, no assumption of order restriction among parameters is required. The order restricted inferences are considered by several authors in different context; for example, the readers are referred to Balakrishnan et al [1], Samanta et al [20], Pal et al [14,13], and Mahto et al [10]. Balakrishnan et al [1] used isotonic regression technique to obtain MLEs of the model parameters under step-stress setup.…”

Section: Introductionmentioning

confidence: 99%

Order Restricted Inference for Adaptive Progressively Censored Competing Risks Data

Ganguly¹,

Mitra²,

Kundu³

2022

Preprint

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Under adaptive progressive Type-II censoring schemes, order restricted inference based on competing risks data is discussed in this article. The latent failure lifetimes for the competing causes are assumed to follow Weibull distributions, with an order restriction on the scale parameters of the distributions. The practical implication of this order restriction is that one of the risk factors is dominant, as often observed in competing risks scenarios. In this setting, likelihood estimation for the model parameters, along with bootstrap based techniques for constructing asymptotic confidence intervals are presented. Bayesian inferential methods for obtaining point estimates and credible intervals for the model parameters are also discussed. Through a detailed Monte Carlo simulation study, the performance of order restricted inferential methods are assessed. In addition, the results are also compared with the case when no order restriction is imposed on the estimation approach. The simulation study shows that order restricted inference is more efficient between the two, when this additional information is taken into consideration. A numerical example is provided for illustrative purpose.

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Inference for partially observed competing risks model for Kumaraswamy distribution under generalized progressive hybrid censoring

Cited by 17 publications

References 39 publications

Statistical reliability analysis of electronic devices using generalized progressively hybrid censoring plan

Statistical reliability analysis of electronic devices using generalized progressively hybrid censoring plan

Bayesian and non‐Bayesian inference of Weibull lifetime model based on partially observed competing risks data under unified hybrid censoring scheme

Order Restricted Inference for Adaptive Progressively Censored Competing Risks Data

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