The accelerated life testing is the key methodology of evaluating product
reliability rapidly. This paper presents statistical inference of Gompertz
distribution based on unified hybrid censored data under constant-stress
partially accelerated life test (CSPALT) model. We apply the stochastic
expectation-maximization algorithm to estimate the CSPALT parameters
and to reduce computational complexity. It is shown that the maximum
likelihood estimates exist uniquely. Asymptotic confidence intervals and
confidence intervals using bootstrap-p and bootstrap-t methods are constructed. Moreover the maximum product of spacing (MPS) and maximum a posteriori (MAP) estimates of the model parameters and accelerated factor are discussed. The performances of the various estimators of
the CSPALT parameters are compared through the simulation study. In
summary, the MAP estimates perform superior than MLEs (or MPSs) with
respect to the smallest MSE values.
CITATIONS(3):
1.
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Enhancing the Fatigue of Mechanical Systems Such as Dispensers Entrenched on Generalized Life-Stress Models and Sample Sizes Seongwoo Woo, Dennis O’Neal, Yury Matvienko, Gezae Mebrahtu Applied Sciences
Influence of the Compliance of a Technological System on the Machining Accuracy of Low-Stiffness Shafts in the Grinding Process Antoni Świć, Arkadiusz Gola Materials
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