Inference and optimal design for the k-level step-stress accelerated life test based on progressive Type-I interval censored power Rayleigh data

Hatim Solayman Migdadi; Nesreen M. Al-Olaimat; Omar Meqdadi; Hatim Solayman Migdadi; Nesreen M. Al-Olaimat; Omar Meqdadi

doi:10.3934/mbe.2023947

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 12: 21407-21431. doi: 10.3934/mbe.2023947

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Research article

Inference and optimal design for the k-level step-stress accelerated life test based on progressive Type-I interval censored power Rayleigh data

1.
Department of Mathematics, Faculty of Science, The Hashemite University, Zarqa 13133, Jordan
2.
Department of Mathematics, Faculty of Science, Ajloun National University, Ajloun 26810, Jordan
3.
Department of Computer Science, University of Wisconsin-Green Bay, Green Bay, USA

Academic Editor: Vladimir Mityushev

Received: 18 August 2023 Revised: 14 November 2023 Accepted: 22 November 2023 Published: 01 December 2023

In this paper, a new generalization of the one parameter Rayleigh distribution called the Power Rayleigh (PRD) was employed to model the life of the tested units in the step-stress accelerated life test. Under progressive Type-I interval censored data, the cumulative exposure distribution was considered to formulate the life model, assuming the scale parameter of PRD has the inverse power function at each stress level. Point estimates of the model parameters were obtained via the maximum likelihood estimation method, while interval estimates were obtained using the asymptotic normality of the derived estimators and the bootstrap resampling method. An extensive simulation study of $ k = 4 $ levels of stress in different combinations of the life test under different progressive censoring schemes was conducted to investigate the performance of the obtained point and interval estimates. Simulation results indicated that point estimates of the model parameters are closest to their initial true values and have relatively small mean squared errors. Accordingly, the interval estimates have small lengths and their coverage probabilities are almost convergent to the 95% significance level. Based on the Fisher information matrix, the D-optimality and the A-optimality criteria are implemented to determine the optimal design of the life test by obtaining the optimum inspection times and optimum stress levels that improve the estimation procedures and give more efficient estimates of the model parameters. Finally, the developed inferential procedures were also applied to a real dataset.
- Power Rayleigh distribution,
- step-stress accelerated life test,
- progressive Type-I interval censored data,
- D-optimality criterion,
- A-optimality criterion
Citation: Hatim Solayman Migdadi, Nesreen M. Al-Olaimat, Omar Meqdadi. Inference and optimal design for the k-level step-stress accelerated life test based on progressive Type-I interval censored power Rayleigh data[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 21407-21431. doi: 10.3934/mbe.2023947

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Abstract

In this paper, a new generalization of the one parameter Rayleigh distribution called the Power Rayleigh (PRD) was employed to model the life of the tested units in the step-stress accelerated life test. Under progressive Type-I interval censored data, the cumulative exposure distribution was considered to formulate the life model, assuming the scale parameter of PRD has the inverse power function at each stress level. Point estimates of the model parameters were obtained via the maximum likelihood estimation method, while interval estimates were obtained using the asymptotic normality of the derived estimators and the bootstrap resampling method. An extensive simulation study of $ k = 4 $ levels of stress in different combinations of the life test under different progressive censoring schemes was conducted to investigate the performance of the obtained point and interval estimates. Simulation results indicated that point estimates of the model parameters are closest to their initial true values and have relatively small mean squared errors. Accordingly, the interval estimates have small lengths and their coverage probabilities are almost convergent to the 95% significance level. Based on the Fisher information matrix, the D-optimality and the A-optimality criteria are implemented to determine the optimal design of the life test by obtaining the optimum inspection times and optimum stress levels that improve the estimation procedures and give more efficient estimates of the model parameters. Finally, the developed inferential procedures were also applied to a real dataset.

References

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