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Adaptive Bayesian Designs for Accelerated Life Testing
Refik Soyer and Anne L. Vopatek
Lecture Notes-Monograph Series
Vol. 25, Adaptive Designs (1995), pp. 263-275
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/4355849
Page Count: 13
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In this paper, we present a Bayesian decision theoretic framework for the design of accelerated life tests. In our development, we assume that quality of inference at the "use stress" is the only concern to the designer and use a quadratic loss function as the design criterion. We derive optimal designs for exponential life models under a given form of an "acceleration function" using a complete test. Linear Bayes methods play an important role in our making inference. Sequential processing of information and the ability to obtain one-point designs make the approach attractive for developing adaptive design strategies.
Lecture Notes-Monograph Series © 1995 Institute of Mathematical Statistics