Surviving fully Bayesian nonparametric regression models
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press
Abstract
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.
Description
Keywords
Bayesian nonparametric models, Accelerated failure time model, Semiparametric survival models, Dirichlet process, Polya tree, Induced survival regression models
Citation
Hanson, T, Jara, A. Surviving fully Bayesian nonparametric regression models. In: Damien, P., Dellaportas P., Polson, N., Stephens, D.,editors. Bayesian Theory and Applications. Oxford, UK: Oxford University Press; 2013. p. 593-615.