The Bernstein--von Mises theorem for the proportional hazard model (2006)
We study large sample properties of Bayesian analysis of the proportional hazard model with neutral to the right process priors on the baseline hazard function. We show that the posterior...
The Bernstein–von Mises theorem for the proportional hazard model (2006)
We study large sample properties of Bayesian analysis of the proportional hazard model with neutral to the right process priors on the baseline hazard function. We show that the posterior...
Learning with Data Adaptive Features (2006)
It is frequently observed that the dimension of inputs is much larger than the sample size. Examples are image construction, microarray data, data mining etc. In such cases, standard learning methods...
On Limiting Posterior Distributions (2005)
Hwang, Hyungtae, So, Beongsoo, Kim, Yongdai
In this article, a theorem is developed on limiting posterior distributions of asymptotically pivotal quantities. The theorem, called the posterior limit theorem (PLT), provides a set of sufficient...
On Limiting Posterior Distributions (2005)
Hwang, Hyungtae, So, Beongsoo, Kim, Yongdai
In this article, a theorem is developed on limiting posterior distributions of asymptotically pivotal quantities. The theorem, called the posterior limit theorem (PLT), provides a set of sufficient...
A Bernstein-von Mises theorem in the nonparametric right-censoring model (2004)
In the recent Bayesian nonparametric literature, many examples have been reported in which Bayesian estimators and posterior distributions do not achieve the optimal convergence rate, indicating that...
Gradient LASSO for feature selection (2004)
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously.
A Bernstein–von Mises theorem in the nonparametric right-censoring model (2004)
In the recent Bayesian nonparametric literature, many examples have been reported in which Bayesian estimators and posterior distributions do not achieve the optimal convergence rate, indicating that...
Bayesian bootstrap for proportional hazards models (2003)
We propose two Bayesian bootstrap extensions, the binomial and Poisson forms, for proportional hazards models. The binomial form Bayesian bootstrap is the limit of the posterior distribution with a...
Bayesian analysis of proportional hazard models (2003)
This paper is concerned with Bayesian analysis of the proportional hazard model with left truncated and right censored data. We use a process neutral to the right as the prior of the baseline...
On posterior consistency of survival models (2001)
Ghosh and Ramamoorthi studied posterior consistency for survival models and showed that the posterior was consistent when the prior on the distribution of survival times was the Dirichlet process...
Bayesian Bootstrap for Proportional Hazards Models (2000)
Bayesian bootstrap was proposed by Rubin (1981) and its theoretical properties and application to survival models without covariates was studies by Lo (1993) and others. Bayesian bootstrap, empirical...
Bayesian Bootstrap for Proportional Hazards Models (1999)
Bayesian bootstrap was proposed by Rubin (1981) and its theoretical properties and application to survival models without covariates was studies by Lo (1993) and others. Bayesian bootstrap, empirical...
Bayesian Bootstrap for Proportional Hazards Models (1999)
Bayesian bootstrap was proposed by Rubin (1981) and its theoretical properties and application to survival models without covariates was studies by Lo (1993) and others. Bayesian bootstrap, empirical...
On Posterior Consistency of Survival Models (1999)
Ghosh and Ramamoorthi (1995) studied the posterior consistency for survival models and showed that the posterior was consistent, when the prior on the distribution of survival times was the Dirichlet...
On Posterior Consistency of Survival Models (1999)
Ghosh and Ramamoorthi (1995) studied the posterior consistency for survival models and showed that the posterior was consistent, when the prior on the distribution of survival times was the Dirichlet...
Semiparametric Bayesian regression model for counting process (1999)
This paper is concerned with a semiparametric Bayesian regression model for the Aalen's multiplicative counting process. A nonparametric prior distribution is put on the baseline intensity function...
Semiparametric Bayesian regression model for multiple event time data (1999)
This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is...
Nonparametric Bayesian estimators for counting processes (1999)
This paper is concerned with nonparametric Bayesian inference of the Aalen’s multiplicative counting process model. For a desired nonparametric prior distribution of the cumulative intensity...
Bayesian inference for censored observations: Posterior inconsistency and its remedy (1970)
In Bayesian paradigm of survival analysis, we can combine a nonparametric estimator and a parametric model by putting a prior distribution nonparametrically around the entire parametric family. This...
On the Posterior Consistency of Mixtures of Dirichlet Process Priors with Censored Data
Mixtures of Dirichlet process priors offer a reasonable compromise between purely parametric and purely non-parametric models, and are popularly used in survival analysis and for testing problems...
On casting random-effects models in a survival framework
Ramani S. Pilla, Yongdai Kim, Hakbae Lee
Logistic random-effects models are often employed in the analysis of correlated binary data. However, fitting these models is challenging, since the marginal distribution of the response variables is...
On limiting posterior distributions
Hyungtae Hwang, Beongsoo So, Yongdai Kim
Asymptotically pivotal quantity, Bernstein-von Mises theorem, posterior limit theorem, 62F03, 62A05,