Reliability Analysis for Masked Data under Type-II Generalized Progressively Hybrid Censored Scheme

The Type-II generalized progressively hybrid censored scheme with masked data is presented. Based on masked system lifetime data, using the expectation maximization algorithm and the Quasi-Newton method, we obtain the Maximum Likelihood Estimation (MLE) of the components distribution parameters in the Weibull case. Finally, Monte Carlo simulation is presented to illustrate the effect.

Inference for Burr-XII Masked System Lifetime Data in Step-Stress Partially Accelerated Life Test with Type-I Progressive Hybrid Censoring

This article considers a step-stress partially accelerated life tests for series system model where independent and non-identical Burr XII-distributed lifetimes are assumed for the components. Based on Type-I progressive hybrid censored and masked data, expectation maximum algorithm combined with auxiliary variables is developed for estimating the model parameters and the acceleration factor. In addition, the asymptotic confidence intervals are constructed by the parametric bootstrap method. Furthermore, the proposed method is illustrated through a simulation study under various masking levels.

Reliability Estimates of Burr XII Components Using Masked Data under Progressive Type-II Censoring

We consider the series system with three independent and non-identical components, each of which has Burr XII distributed lifetime. Based on progressively type-II censored and masked system lifetime data, the maximum likelihood estimates (MLE) of the components parameters and reliability function are obtained. By introducing a latent variable, Bayesian estimators of the components parameters and the reliability function are also developed using Gibbs sampling method. Furthermore, in the numerical simulation study, the MLE and Bayesian estimates are compared under different removal probabilities and different times.