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
Bayesian Semiparametric Regression for Median Residual Life
TL;DR: In this paper, a semi-parametric median residual life regression model is proposed for small cell lung cancer patients with moderate censoring, which is based on Dirichlet process mixing.
Journal ArticleDOI
A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties
TL;DR: A Bayesian semiparametric model for capturing spatio-temporal heterogeneity within the proportional hazards framework is proposed and an autoregressive dependent tailfree process is introduced.
Book ChapterDOI
Efficient MCMC Schemes for Robust Model Extensions Using Encompassing Dirichlet Process Mixture Models
TL;DR: It is proposed that one consider sensitivity analysis by embedding standard parametric models in model extensions defined by replacing a parametric probability model with a nonparametric extension.
Journal ArticleDOI
A Bayesian Semiparametric AFT Model for Interval-Censored Data
Timothy Hanson,Wesley O. Johnson +1 more
TL;DR: A novel MCMC scheme is introduced for the purpose of making posterior inferences for the AFT regression model and is viewed as a simple extension of existing parametric models.
Journal ArticleDOI
Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models
TL;DR: A nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event and requires little user input in terms of model specification for treatment covariate interactions or for tuning parameter selection.
References
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Inference from Iterative Simulation Using Multiple Sequences
Andrew Gelman,Donald B. Rubin +1 more
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Regression models and life tables (with discussion
TL;DR: The drum mallets disclosed in this article are adjustable, by the percussion player, as to balance, overall weight, head characteristics and tone production of the mallet, whereby the adjustment can be readily obtained.
Journal ArticleDOI
The Statistical Analysis of Failure Time Data.
Journal ArticleDOI
A Bayesian Analysis of Some Nonparametric Problems
TL;DR: In this article, a class of prior distributions, called Dirichlet process priors, is proposed for nonparametric problems, for which treatment of many non-parametric statistical problems may be carried out, yielding results that are comparable to the classical theory.