Reliability Estimation in Series Systems: Maximum Likelihood Techniques for Right-Censored and Masked Failure Data

@mastersthesis{Towell2023,
  author       = {Towell, Alexander R.},
  abstract     = {This paper investigates maximum likelihood techniques to estimate component reliability from masked
failure data in series systems. A likelihood model accounts for right-censoring and candidate sets
indicative of masked failure causes. Extensive simulation studies assess the accuracy and precision of
maximum likelihood estimates under varying sample size, masking probability, and right-censoring time
for components with Weibull lifetimes. The studies specifically examine the accuracy and precision of
estimates, along with the coverage probability and width of BCa confidence intervals. Despite significant
masking and censoring, the maximum likelihood estimator demonstrates good overall performance. The
bootstrap yields correctly specified confidence intervals even for small sample sizes. Together, the modeling
framework and simulation studies provide rigorous validation of statistical learning from masked reliability
data.},
  title        = {Reliability Estimation in Series Systems: Maximum Likelihood Techniques for Right-Censored and Masked Failure Data},
  school       = {Southern Illinois University at Edwardsville},
  year         = {2023},
  type         = {Thesis},
  doi          = {10.5281/zenodo.15151227},
  url          = {https://doi.org/10.5281/zenodo.15151227}
}