A dynamic general linear model for inference from accelerated life tests

The paper presents a new approach for inference from accelerated life tests. The present approach is based on a dynamic general linear model setup which arises naturally from the accelerated life-testing problem and uses linear Bayesian methods for inference. The advantage of the procedure is that it does not require large numbers of items to be tested and that it can deal with both censored and uncensored data. The paper illustrates the use of the present approach with some actual accelerated life-test data.