Expectation-Maximization Algorithms for Obtaining Estimations of Generalized Failure Intensity Parameters

This paper presents several iterative methods based on Stochastic Expectation-Maximization (EM) methodology in order to estimate parametric reliability models for randomly lifetime data. The methodology is related to Maximum Likelihood Estimates (MLE) in the case of missing data. A bathtub form of failure intensity formulation of a repairable system reliability is presented where the estimation of its parameters is considered through EM algorithm . Field of failures data from industrial site are used to fit the model. Finally, the interval estimation basing on large-sample in literature is discussed and the examination of the actual coverage probabilities of these confidence intervals is presented using Monte Carlo simulation method.

@article{KRIT2016,
  title     = {Expectation-Maximization Algorithms for Obtaining Estimations of Generalized Failure Intensity Parameters},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {1},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Makram KRIT and Khaled MILI},
  doi       = {10.14569/IJACSA.2016.070158},
  url       = {https://doi.org/10.14569/IJACSA.2016.070158}
}