A semi-parametric Bayesian analysis of survival data based on levy-driven processes

Nieto-Barajas, Luis E. and Walker, Stephen G. (2005) A semi-parametric Bayesian analysis of survival data based on levy-driven processes. Lifetime Data Analysis, 11 (4). pp. 529-543. ISSN 1380-7870. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/s10985-005-5238-7

Abstract

In the presence of covariate information, the proportional hazards model is one of the most popular models. In this paper, in a Bayesian nonparametric framework, we use a Markov (Levy-driven) process to model the baseline hazard rate. Previous Bayesian nonparametric models have been based on neutral to the right processes, which have a number of drawbacks, such as discreteness of the cumulative hazard function. We allow the covariates to be time dependent functions and develop a full posterior analysis via substitution sampling. A detailed illustration is presented.

Item Type: Article
Uncontrolled keywords: Bayes nonparametrics; Levy-driven process; Markov process; survival analysis; proportional hazards model; time-dependent covariates COX REGRESSION-MODEL; NONPARAMETRIC-ESTIMATION; POSTERIOR DISTRIBUTIONS; GAMMA PROCESSES; LARGE SAMPLE; REPRESENTATION; BETA
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Judith Broom
Date Deposited: 11 Sep 2008 15:34
Last Modified: 25 Jun 2014 10:44
Resource URI: http://kar.kent.ac.uk/id/eprint/10539 (The current URI for this page, for reference purposes)
  • Depositors only (login required):