Nieto-Barajas, Luis E., 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. (doi:10.1007/s10985-005-5238-7) (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) (KAR id:10539)
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. | |
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 |
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DOI/Identification number: | 10.1007/s10985-005-5238-7 |
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: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Depositing User: | Judith Broom |
Date Deposited: | 11 Sep 2008 15:34 UTC |
Last Modified: | 05 Nov 2024 09:43 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/10539 (The current URI for this page, for reference purposes) |
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