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 currently available from this repository. You may be able to access a copy if URLs are provided)

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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: https://kar.kent.ac.uk/id/eprint/10539 (The current URI for this page, for reference purposes)
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