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Bayesian computations in survival models via the Gibbs sampler

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Author
Kuo, Lynn.
Smith, Adrian F. M.
Date
1991-07
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Abstract
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
URI
http://hdl.handle.net/10945/28689
NPS Report Number
NPS-OR-91-020
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  • All Technical Reports Collection
  • Operations Research (NPS-OR)
NPS Dudley Knox LibraryDUDLEY KNOX LIBRARY
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