Likelihood Estimation of Exponentiated Exponential Distribution under Step Stress Partially Accelerated Life Testing Plan Using Progressive Type-I Censoring

2017 ◽  
Vol 5 (3) ◽  
pp. 107-112
Author(s):  
Showkat Ahmad Lone ◽  
Ahmadur Rahman ◽  
Arif-Ul- Islam
Keyword(s):  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Accelerated Life Testing ◽  
Type I ◽  
Life Testing ◽  
Type I Censoring ◽  
Accelerated Life ◽  
Progressive Type ◽  
Testing Plan

scholarly journals MCMC Method for Exponentiated Lomax Distribution based on Accelerated Life Testing with Type I Censoring

2021 ◽  
Vol 20 ◽  
pp. 319-334
Author(s):  
Refah Alotaibi ◽  
H. Rezk ◽  
Sanku Dey
Keyword(s):  
Accelerated Life Testing ◽  
Constant Stress ◽  
Estimation Methods ◽  
Model Parameters ◽  
Type I ◽  
Life Testing ◽  
Informative Priors ◽  
Lomax Distribution ◽  
Type I Censoring ◽  
Accelerated Life

Accelerated Life Testing (ALT) is an effective technique which has been used in different fields to obtain more failures in a shorter period of time. It is more economical than traditional reliability testing. In this article, we propose Bayesian inference approach for planning optimal constant stress ALT with Type I censoring. The lifetime of a test unit follows an exponentiated Lomax distribution. Bayes point estimates of the model parameters and credible intervals under uniform and log-normal priors are obtained. Besides, optimum test plan based on constant stress ALT under Type I censoring is developed by minimizing the pre-posterior variance of a specified low percentile of the lifetime distribution at use condition. Gibbs sampling method is used to find the optimal stress with changing time. The performance of the estimation methods is demonstrated for both simulated and real data sets. Results indicate that both the priors and the sample size affect the optimal Bayesian plans. Further, informative priors provide better results than non-informative priors.

scholarly journals Bayesian estimation of the parameters of the bivariate generalized exponential distribution using accelerated life testing under censoring data

2015 ◽  
Vol 3 (2) ◽  
pp. 215
Author(s):  
Abd El-Maseh, M. P
Keyword(s):  
Bayesian Estimation ◽  
Accelerated Life Testing ◽  
Constant Stress ◽  
Type I ◽  
Life Testing ◽  
Unknown Parameters ◽  
Numerical Studies ◽  
Inverse Power Law ◽  
Accelerated Life ◽  
Power Law Function

<p>In this paper, the Bayesian estimation for the unknown parameters for the bivariate generalized exponential (BVGE) distribution under Bivariate censoring type-I samples with constant stress accelerated life testing (CSALT) are discussed. The scale parameter of the lifetime distribution at constant stress levels is assumed to be an inverse power law function of the stress level. The parameters are estimated by Bayesian approach using Markov Chain Monte Carlo (MCMC) method based on Gibbs sampling. Then, the numerical studies are introduced to illustrate the approach study using samples which have been generated from the BVGE distribution.</p>

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