In this article, we consider step-stress accelerated life testing (ssalt) models assuming that the time-to-event distribution belongs to the proportional hazard family and the underlying population consists of long-te...
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In this article, we consider step-stress accelerated life testing (ssalt) models assuming that the time-to-event distribution belongs to the proportional hazard family and the underlying population consists of long-term survivors. Further, with an increase in stress levels, it is natural that the mean time to the event of interest gets shortened and hence a method of obtaining order-restricted maximum likelihood estimators (MLEs) of the model parameters is proposed based on expectation maximization (EM) algorithm coupled with the reparametrization technique. To illustrate the effectiveness of the proposed method, extensive simulation experiments are performed and a real-life data example is analyzed in detail.
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