Reliability analysis of log-normal distribution with nonconstant parameters under constant-stress model

Wei Cui, Zai-zai Yan (), Xiu-yun Peng and Gai-mei Zhang
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Wei Cui: Jilin University of Finance and Economics
Zai-zai Yan: Inner Mongolia University of Technology
Xiu-yun Peng: Inner Mongolia University of Technology
Gai-mei Zhang: Huhhot First Hospital

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 2, No 20, 818-831

Abstract: Abstract Under constant-stress accelerated life test, the general progressive type-II censoring sample and the two parameters following the linear Arrhenius model, the point estimation and interval estimation of the two parameters log-normal distribution were discussed. The unknown parameters of the model as well as reliability and hazard rate functions are estimated by using Maximum likelihood (ML) and Bayesian methods. The maximum-likelihood estimates are derived by the Newton–Raphson method and the corresponding asymptotic variance is derived by the Fisher information matrix. Since the Bayesian estimates (BEs) of the unknown parameters cannot be expressed explicitly, the approximate BEs of the unknown parameters. The approximate highest posterior density confidence intervals are calculated. The practicality of the proposed method is illustrated by simulation study and real data application analysis.

Keywords: Constant-stress accelerated life test; Non-constant parameters; Bayesian estimation; Markov Chain Monte Carlo (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01343-0

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