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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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Surviving fully Bayesian nonparametric regression models

Surviving fully Bayesian nonparametric regression models

Chapter:
(p.593) 30 Surviving fully Bayesian nonparametric regression models
Source:
Bayesian Theory and Applications
Author(s):

Timothy E. Hanson

Alejandro Jara

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0030

This chapter compares two Bayesian nonparametric models that generalize the accelerated failure time model, based on recent work on probability models for predictor-dependent probability distributions. It begins by reviewing commonly used semiparametric survival models. It then discusses the Bayesian nonparametric priors used in the generalizations of the accelerated failure time (AFT) model. Next, the two generalizations of the accelerated failure time model are introduced and compared by means of real-life data analyses. The models correspond to generalizations of AFT models based on dependent extensions of the Dirichlet process (DP) and Polya tree (PT) priors. Advantages of the induced survival regression models include ease of interpretability and computational tractability.

Keywords:   Bayesian nonparametric models, accelerated failure time model, semiparametric survival models, Dirichlet process, Polya tree, induced survival regression models

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