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
Nonparametric Bayesian inference for mean residual life functions in survival analysis.
Valerie Poynor,Athanasios Kottas +1 more
TL;DR: In this article, the authors developed general Bayesian nonparametric inference for mean residual life (MRL) functions built from a Dirichlet process mixture model for the associated survival distribution.
Journal Article
Accelerated Failure Time Models: A Review
TL;DR: This paper focuses on accelerated failure time models and on the related statistical inference, and describes some open questions and future research directions.
Journal ArticleDOI
A Nonparametric Bayesian Technique for High-Dimensional Regression
TL;DR: This paper proposes a nonparametric Bayesian framework called VariScan for simultaneous clustering, variable selection, and prediction in high-throughput regression settings and demonstrates that VariScan often outperforms several well-known statistical methods.
Dissertation
Bayesian Nonparametric Dirichlet Process Mixture Modeling in Transportation Safety Studies
Journal ArticleDOI
Indicator Generalized Parameterization for Interpolation Point Selection in Groundwater Inverse Modeling
TL;DR: In this article, the authors developed an indicator generalized parameterization (IGP) method to cope with the problem of selecting interpolation points in estimating hydraulic conductivity fields, where data indicators and d-neighborhoods were introduced to describe the actual contribution of sample data to unsampled locations.
References
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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
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