Linear Inference for Type-II Censored Lifetime Data of Reliability Systems With Known Signatures

2011 ◽  
Vol 60 (2) ◽  
pp. 426-440 ◽  
Author(s):  
Narayanaswamy Balakrishnan ◽  
Hon Keung Tony Ng ◽  
Jorge Navarro
Keyword(s):  
Lifetime Data ◽  
Type Ii ◽  
Reliability Systems ◽  
Linear Inference

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

2014 ◽  
Vol 687-691 ◽  
pp. 1198-1201
Author(s):  
Bin Liu ◽  
Yi Min Shi ◽  
Jing Cai ◽  
Mo Chen
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Lifetime Data ◽  
Type Ii ◽  
Distribution Parameters ◽  
System Lifetime Data ◽  
Quasi Newton

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.

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

2014 ◽  
Vol 551 ◽  
pp. 626-632
Author(s):  
Mo Chen ◽  
Yi Min Shi ◽  
Li Jin
Keyword(s):  
Latent Variable ◽  
Sampling Method ◽  
Reliability Function ◽  
Maximum Likelihood Estimates ◽  
Lifetime Data ◽  
Series System ◽  
Type Ii ◽  
Reliability Estimates ◽  
Progressive Type ◽  
System Lifetime Data

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.

scholarly journals A Multiple Dependent State Repetitive Sampling Plan Based on Performance Index for Lifetime Data with Type II Censoring

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 49377-49391 ◽  
Author(s):  
Muhammad Aslam ◽  
Ching-Ho Yen ◽  
Chia-Hao Chang ◽  
Ali Hussein Al-Marshadi ◽  
Chi-Hyuck Jun
Keyword(s):  
Performance Index ◽  
Sampling Plan ◽  
Lifetime Data ◽  
Type Ii ◽  
Type Ii Censoring ◽  
Dependent State

Reliability Inference Based on the Three-Parameter Burr Type XII Distribution with Type II Censoring

2018 ◽  
Vol 25 (02) ◽  
pp. 1850010 ◽  
Author(s):  
Hua Xin ◽  
Jianping Zhu ◽  
Junge Sun ◽  
Chenlu Zheng ◽  
Tzong-Ru Tsai
Keyword(s):  
Monte Carlo ◽  
Parametric Bootstrap ◽  
Maximum Likelihood Estimates ◽  
Oil Well ◽  
Lifetime Data ◽  
Type Ii ◽  
Breast Cancer Patients ◽  
Mcmc Method ◽  
Wide Range ◽  
Burr Type Xii

The three-parameter Burr type XII distribution (3pBXIID) is quite flexible and contains a wide range of distribution shapes for fitting lifetime data. However, it is difficult to obtain reliable estimates of the 3pBXIID quantiles from censored samples for evaluating the reliability of lifetime data. In this work, a Metropolis–Hastings Markov chain Monte Carlo (M-H MCMC) procedure is proposed to obtain reliable maximum likelihood estimates (MLEs) of the 3pBXIID quantiles from a type II censored sample. Moreover, the parametric bootstrap percentile procedure is used to obtain the confidence interval of the quantile of the 3pBXIID. The performance of the proposed M-H MCMC method is evaluated in view of Monte Carlo simulations. Two examples, regarding the survival lifetimes of breast cancer patients and the reliability inference on the lifetimes of oil-well pumps for sucker-rod oil pumping systems, are applied to illustrate the applications of the proposed M-H MCMC method and bootstrap procedure.

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