Bayesian Inference Under Ramp Stress Accelerated Life Testing Using Stan

Abdalla Abdel-Ghaly (), Hanan Aly () and Elham Abdel-Rahman ()
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Abdalla Abdel-Ghaly: Cairo University
Hanan Aly: Cairo University
Elham Abdel-Rahman: Cairo University

Sankhya B: The Indian Journal of Statistics, 2023, vol. 85, issue 1, No 6, 132-174

Abstract: Abstract In this paper, the implementations of No-U-Turn Sampler (NUTS), an extension of Hamiltonian Monte Carlo (HMC) method, via Stan software is considered for the first time under ramp stress accelerated life testing (RS-ALT). Assuming an extended Weibul (EW) distribution in the presence of adaptive type-II progressive censoring (A-II-PC) scheme, NUTS is adopted to obtain point and interval Bayesian estimation for the unknown parameters and acceleration factors when the scale parameter is related to stress through inverse power law relationship. One-sample and two-sample prediction problems are also studied under the same framework using two different approaches. To asses the performance of the suggested methods, a Monte Carlo simulation study is conducted. Finally, a real data example is provided to illustrate the application of the proposed methods in reality.

Keywords: Ramp-stress; Extended Weibull distribution; Hamiltonian Monte Carlo (HMC); Stan software; Bayes prediction; adaptive type-II progressive censoring.; Primary 62N05; Secondary 62F15 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13571-022-00300-6

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