| Summary: | 碩士 === 國立中央大學 === 統計研究所 === 102 === High reliability products have longer lifetime under normal environment. Accelerated life tests are
usually used to reduce the experiment time. In a series system, the system fails when any of the
components fails, while the cause of system failure may not be observed which is known as masked
data. In this thesis, we consider the step-stress accelerated life tests for series systems with Type-I
censoring, in which the lifetimes of components are exponentially distributed. We not only consider
those distributions are independent, but also consider the Marshall-Olkin bivariate distribution for
two-components series systems. Assume that there exists log-linear relationship between the mean
lifetime of components and the levels of the environmental stress variables under the cumulative exposure model, and the data analyzed are interval data in the sense only the numbers of failures are observed at the times of changing stress levels. Maximum likelihood inference is developed
incorporated with the EM algorithm as well as the missing information principle to achieving the Fisher information. Simulation study is carried out in the reliability analysis. It shows that the proposed method is more accurate and efficient than the bootstrap method.
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