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An Inverse Gaussian Process Model for Degradation Data

Summary: [This abstract is based on the authors' abstract.] The maximum likelihood estimate of a class of inverse Gaussian process models for degradation data is considered which may be extended to incorporate random effects and covariates. The EM algorithm is used to obtain the maximum likelihood estimators of the unknown parameters, and the bootstrap is used to assess their variability. The model is fitted to laser data and goodness-of-fit tests are conducted. In addition, failure time distributions in terms of degradation level passages are calculated and illustrated.

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  • Topics: Reliability
  • Keywords: Bootstrap methods, Degradation, Empirical model, Random effects, Reliability, Maximum likelihood estimate (MLE), Goodness of fit
  • Author: Wang, Xiao; Xu, Dihua
  • Journal: Technometrics