Wj. Zimmer et Jj. Deely, A BAYES RANKING OF SURVIVAL DISTRIBUTIONS USING ACCELERATED OR CORRELATED DATA, IEEE transactions on reliability, 45(3), 1996, pp. 499-504
Summ. & Conclusions - The high reliability of modern components requir
es accelerated testing to compare or predict survival or failure rates
in the use condition, If the testing of highly reliable components is
done in the use condition, the times to failure are too long to obser
ve, Therefore, it is often required to compare or predict mean times t
o failure in the use condition when the only available data are from a
highly accelerated condition. Comparison of failure rates in this sit
uation is possible using frequentist methods but estimation of the ind
ividual failure rates Is not, Using Bayes methods, both comparison and
prediction results are easily possible & computable. The accelerated
model in this paper is similar to the model used in health-related res
earch when the data are from a paired experiment, This use of the mode
l to compare & predict survival rates using paired data is also handle
d easily by the Bayes approach. Let the two failure rates be lambda(1)
, lambda(2); the results are presented as the posterior probability Pr
{lambda(1) < c .lambda(2)\data}, 0 < c less than or equal to 1. The pr
edictions are the posterior means, E{lambda(1)\data}, E{lambda(2)\data
}.