AN IMPRECISE DIRICHLET MODEL FOR BAYESIAN-ANALYSIS OF FAILURE DATA INCLUDING RIGHT-CENSORED OBSERVATIONS

This paper is intended to make researchers in reliability theory aware of a recently introduced Bayesian model with imprecise prior distribu tions for statistical inference on failure data, that can also be cons idered as a robust Bayesian model. The model consists of a multinomial distribution with Dirichlet priors, making the approach basically non parametric. New results for the model are presented, related to right- censored observations. where estimation based on this model is closely related to the product-limit estimator, which is an important statist ical method to deal with reliability or survival data including right- censored observations. As for the product-limit estimator, the model c onsidered in this paper aims at not using any information other than t hat provided by observed data, but our model fits into the robust Baye sian context which has the advantage that all inferences can be based on probabilities or expectations, or bounds for probabilities or expec tations. The model uses a finite partition of the time-axis, and as su ch it is also related to life-tables. (C) 1997 Elsevier Science Limite d.