Inference from Accelerated Life Tests Using Filtering in Coloured Noise

SUMMARY We present a new approach for inference from accelerated life tests. Our approach formulates such problems as inference under Kalman filter models with correlated observation errors. We restrict attention to exponential life distributions, and the power rule as a time transformation function. Extensions to other time transformation functions are straightforward; however, extensions to other distributions involve nonlinear filtering and are not considered. The advantages of our formulation are that we are able to incorporate uncertainty in the time transformation function and also are able to allow it to change with the stress. to validate our approach we consider some simulated data; to give it a sense of reality we apply it to actual data.