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
Non parametric Bayesian analysis of the two-sample problem with censoring
Kan Shang,Cavan S. Reilly +1 more
TL;DR: In this paper, the Dirichlet process prior and the Wilcoxon rank sum test bucht are used to test for differences between two groups, and a new approach to developing such tests and a class of such tests that takes advantage of developments in Bayesian nonparametric computing is described.
Using integrated reliability analysis to optimise maintenance strategies : a Bayesian integrated reliability analysis of locomotive wheels
TL;DR: The goal of the research presented in this report is to propose, develop and test an integrated reliability analysis to optimise the maintenance strategies of the railway industry.
Journal ArticleDOI
Bayesian semiparametric failure time models for multivariate censored data with latent variables.
TL;DR: A semiparametric failure time model to analyze multivariate censored data with latent variables is proposed and a Bayesian approach, along with Bayesian P-splines and Markov chain Monte Carlo techniques, is developed to estimate the unknown parameters and functions.
Journal ArticleDOI
On choosing the centering distribution in Dirichlet process mixture models
TL;DR: The results indicate that for Gaussian kernels, one can choose the centering measure for the Dirichlet process mixture model exactly as one would in the analogous simpler Dirich let process model.
Book ChapterDOI
Bayesian Nonparametric Biostatistics
TL;DR: This work discusses some typical applications of Bayesian nonparametrics in biostatistics and reviews some modern Bayesian semi- and nonparametric approaches for modeling longitudinal, survival, and medical diagnostic outcome data.
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.