Journal ArticleDOI
Bayesian semiparametric inference for the accelerated failure‐time model
Lynn Kuo,Bani K. Mallick +1 more
TLDR
In this paper, a Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution of a log-linear model, and a model selection method for obtaining a more parsimonious set of predictors is studied.Abstract:
Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.read more
Citations
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Journal ArticleDOI
An improved collapsed Gibbs sampler for Dirichlet process mixing models
Lynn Kuo,Tae Young Yang +1 more
TL;DR: A new Gibbs sampler is developed that improves upon the current collapsed GibbsSampler by blocking and reducing the number of classification probabilities to be updated using the clustering configuration.
Journal ArticleDOI
Flexible extension of the accelerated failure time model to account for nonlinear and time-dependent effects of covariates on the hazard.
Menglan Pang,Robert W. Platt,Robert W. Platt,Tibor Schuster,Michal Abrahamowicz,Michal Abrahamowicz +5 more
TL;DR: The accelerated failure time model is an alternative to the Cox proportional hazards model in survival analysis as mentioned in this paper, however, conclusions regarding the associations of prognostic factors with event timings are questionable.
Journal ArticleDOI
A Bayesian semiparametric accelerated failure time model for arbitrarily censored data with covariates subject to measurement error
TL;DR: A flexible Bayesian semiparametric accelerated failure time (AFT) model is proposed for analyzing arbitrarily censored survival data with covariates subject to measurement error, and the baseline error distribution is nonparametrically modeled as a Dirichlet process mixture of normals.
Book ChapterDOI
Mixed-effects modelling of Kevlar fibre failure times through Bayesian non-parametrics
TL;DR: In this article, a semi-parametric model for failure times of Kevlar fibres grouped by spool, subject to different stress levels, is proposed, where the error distribution is a shape-scale mixture of Weibull densities, the mixing measure being a normalised generalised gamma measure.
OtherDOI
Accelerated Life Tests: Bayesian Models†
TL;DR: This article develops Markov chain Monte Carlo methods to make Bayesian inferences from accelerated life test models where the effect of accelerated environments on failure behavior is described by time transformation functions.
References
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Journal ArticleDOI
The Statistical Analysis of Failure Time Data.
Journal ArticleDOI
A Bayesian Analysis of Some Nonparametric Problems
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