Abstract not found

Multivariate event time data arises frequently in both medical and industrial settings. In such data sets: event times may be associated with quite different occurrences, event times can not be considered as independent - the distribution of time to occurrence of one event may change after the occurrence of another, events can occur simultaneously, available covariate information may provide useful explanation. Censoring in some of the observations, both partial and complete, occurs. Focusing on the bivariate case, we formulate models rich enough to accommodate these features. In the spirit of fatal shock models our classes are built using latent waiting times which are assumed to follow general proportional hazards or accelerated life models. We adopt a Bayesian perspective for inference using simulation based fitting which routinely handles censoring. Since a wide range of model specifications can be introduced, we propose a generic model selection criterion for choosing among bivari...

• “Decision-based methods for forecast evaluation”, (with M. Hashem Pesaran) in Companion to Economic Forecasting, M.P. Clements and D.F. Hendry (Eds), 2001,

Model selection is an important part of any statistical analysis, and indeed is central to the pursuit of science in general. Many authors have examined this question, from both frequentist and Bayesian perspectives, and many tools for selecting the “best model ” have been suggested in the literature. This paper considers the various proposals from a Bayesian decision–theoretic perspective.