Abstract. If masking happens, we can only observe the system life time and the set of components that containing the real culprit for the failure. Estimation of the parameters included in the lifetime distribution of the individual components in a Poisson shock model is introduced in this paper by using masked system life data. We deduce the maximum likelihood and Bayes estimation of these parameters, as well as the survival function of each component. And a numerical simulation study is introduced to compare the influence of the masking level on the accuracy of the estimation.

Received: March 26, 2011

AMS Subject Classification: 90B25

Key Words and Phrases: Poisson shock model, maximum likelihood estimation, Bayes estimation, masked data

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