Analysis of simple step-stress model in presence of competing risks

Ganguly, Ayon; Kundu, Debasis

doi:10.1080/00949655.2015.1096362

Cited by 39 publications

(15 citation statements)

References 37 publications

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“…It should be noted that further inferential problems can be studied for the discussed model. For instance, the subsequent topics may be investigated for k ‐step SLT by analogy with the following references discussing simple step stress testing: alternative lifetime distributions like two‐parameter exponential or Weibull distributions (see, e.g., Kateri & Balakrishnan, 2008; Mitra et al, 2013), other inferential methods like Bayesian approaches or order restricted inference (see, e.g., Samanta, Ganguly, Kundu, & Mitra, 2017; Samanta, Kundu, & Ganguly, 2018; Samanta, Ganguly, Gupta, & Kundu, 2019) or more advanced models like competing risks (see, e.g., Balakrishnan & Han, 2008; Ganguly & Kundu, 2016).…”

Section: Maximum Likelihood Estimation Under K‐step Sltmentioning

confidence: 99%

k‐step stage life testing

Laumen

Cramer

2020

Statistica Neerlandica

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The model of stage life testing proposed in Laumen and Cramer (2019b) is extended from two to k stages. It is illustrated that this model can be seen as an extension of progressive censoring with fixed censoring times as well as of simple step stress testing. Extending the first model, the new approach allows to incorporate information from progressively censored units subject to additional life testing. On the other hand, simple step stress modeling is generalized in the sense that only parts of the units under test are put on another stress level whereas the others remain on the initial level. The model is further studied for exponential lifetimes assuming a cumulative exposure model. We obtain the exact (conditional) distribution of the maximum likelihood estimators which is applied to construct (exact) confidence intervals for the distribution parameters. Finally, we investigate the situation of Weibull distributed lifetimes on the initial stage. The study is supplemented by illustrative simulations.

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Section: Maximum Likelihood Estimation Under K‐step Sltmentioning

confidence: 99%

k‐step stage life testing

Laumen

Cramer

2020

Statistica Neerlandica

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“…The likelihood equations can be obtain from (14) by taken the first partial derivatives to a, b 1 , b 2 and β as follows…”

Section: The Point Mlesmentioning

confidence: 99%

“…This problem known as the competing risks problem. Different work is discussed the problem of the analysis of competing risks model, see, Cox [10], David and Moeschberger [11], Crowder [12], Balakrishnan and Han [13] and Ganguly and Kundu [14]. In this paper, the model of the partially step-stress ALTs is applied when the units are fails due to two risk factor.…”

Section: Introductionmentioning

confidence: 99%

Competing Risks Model with Partially Step-Stress Accelerate Life Tests in Analyses Lifetime Chen Data under Type-II Censoring Scheme

Abu-Zinadah

Sayed-Ahmed

2019

Open Physics

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The experiment design may need a stress level higher than use condition which is called accelerate life tests (ALTs). One of the most ALTs appears in different applications in the life testes experiment is partially step stress ALTs. Also, the experiment items is failure with several fatal risk factors, the only one is caused to failure which called competing risk model. In this paper, the partially step-stress ALTs based on Type-II censoring scheme is adopted under the different risk factors belong to Chen lifetime distributions. Under this assumption, we will estimate the model parameters of the different causes with the maximum likelihood method. The two, asymptotic distributions and the parametric bootstrap will be used to build each confidence interval of the model parameters. The precision results will be assessed through Monte Carlo simulation study.

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“…e effect of any risk factor in the presence of other risk factors need to be assessed. is problem has been discussed early in [6][7][8][9][10] and recently in [11]. Under the consideration of two causes of failure, the competing risks model in the presence of type-I GHCS is presented as follows.…”

Section: Introductionmentioning

confidence: 99%

Generalized Type-I Hybrid Censoring Scheme in Estimation Competing Risks Chen Lifetime Populations

Sayed-Ahmed

Jawa

Aloafi

et al. 2021

Mathematical Problems in Engineering

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Different types of censoring scheme are investigated; however, statistical inference on censoring scheme which can save the ideal test time and the minimum number of failures is needed. The generalized type-I hybrid censoring scheme (GHCS) solves this problem. Competing the risk models under the GHCS when time to failure has Chen lifetime distribution (CD) is adopted in this research with consideration of only two cases of failure. Partially step-stress accelerated life tests (ALTs) are applied to obtain enough failure times in a small period to achieve a highly reliable product. The problem of parameter estimation under maximum likelihood (ML) and Bayes methods is discussed. The asymptotic confidence interval as well as the Bayes credible interval is constructed. The validity of theoretical results is assessed and compared through simulation study. Finally, brief comments are reported to describe the behaviour of the estimation results.

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Analysis of simple step-stress model in presence of competing risks

Cited by 39 publications

References 37 publications

k‐step stage life testing

k‐step stage life testing

Competing Risks Model with Partially Step-Stress Accelerate Life Tests in Analyses Lifetime Chen Data under Type-II Censoring Scheme

Generalized Type-I Hybrid Censoring Scheme in Estimation Competing Risks Chen Lifetime Populations

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