A Bayesian Approach for Accelerated Failure Time Model with Skewed Normal Error

Bayesian Accelerated Failure Time Model for Correlated Interval-censored Data with a Normal Mixture as Error Distribution

A Bayesian approach is proposed for an accelerated failure time model with interval-censored data. The model allows for structured correlated data by inclusion of a random effect part that might depend on covariates, as in a linear mixed model. The error distribution is modelled as a normal mixture with an unknown number of components. Also, the means and variances of the components are not pre...

Bayesian Semiparametric Inference for the Accelerated Failure Time Model

Bayesian semi-parametric inference is considered for a log-linear model. This model consists of a parametric component for the regression coeecients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coeecients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov ch...

Monitoring Lognormal Reliability Data in a Two-Stage Process Using Accelerated Failure Time Model

The reliability data is getting used to monitor and improve the quality of products or services. Nowadays, most of products or services are the results of processes with dependent stages referred to as multi-stage process. In these processes, the quality characteristics are affected by the quality characteristics in the previous stages, called as cascade property. In some cases, it is not possi...

Estimation Methods for Accelerated Failure Time Model

In the literature, a lot of effort has been devoted to develop effective estimation and inference methods for the accelerated failure time (AFT) model for right censored data. In the talk, we will give a review on the recent development on the estimation and inference methods for the AFT model based on the work in [Jin et al., 2003] and [Jin et al., 2004].

Bayesian Accelerated Failure Time Model with Multivariate Doubly-Interval-Censored Data and Flexible Distributional Assumptions