Bayesian nonparametric estimation in a series system or a competing-risks model

Bayesian Nonparametric Estimation in a Series System or a Competing-Risks Model

This paper presents a Bayesian nonparametric approach to the estimation of a system and its components’ survival functions arising from observing the failure of a series system or a competing-risks model. A Dirichlet multivariate process is used as a prior for the vector of the components’ random subsurvival function to derive the Bayes estimator of the survival function when the cause of failu...

Parametric Estimation in a Recurrent Competing Risks Model

A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the compet- ing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. ...

Nonparametric Estimation for Current Status Data with Competing Risks

Nonparametric estimation with left truncated semi-competing risks data

SUMMARY Cause-specific hazard and cumulative incidence function are of practical importance in competing risks studies. Inferential procedures for these quantities are well developed and can be applied to semi-competing risks data, where a terminating event censors a non-terminating event, after coercing the data into the competing risks format. Complications arise when there is left truncation...

Parametric Estimation in a Recurrent Competing Risks Model.

A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the competing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. Ma...