Bayesian computations in survival models via the Gibbs sampler
By: Lynn Kuo, and Adrian F.M. Smith
Contributors: Smith, Adrian F. M.; Naval Postgraduate School (U.S.). Dept. of Operations Research
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1991
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Subjects: Statistical theory; Bayes theorem; Statistical samples
Other titles: NPS-OR-91-020.
Description: Survival models used in biomedical and reliability contexts typically involve data censoring, and may also involve constraints in the form of ordered parameters. In addition, inferential interest often focuses on non-linear functions of natural model parameters. From a Bayesian statistical analysis perspective, these features combine to create difficult computational problems by seeming to require (multi-dimensional) numerical integrals over awkwardly defined regions. This paper illustrates how these apparent difficulties can be overcome, in both parametric and non-parametric settings, by the Gibbs sampler approach to Bayesian computation.
Notes: Title from cover.

"NPS-OR-91-020."

"July 1991."

AD A242 343.

Includes bibliographical references.

aq/aq cc:9116 04/19/99.