Nieto-Barajas, L.E. and Walker, S.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.
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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.
|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:||14 Jan 2010 14:40|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/10539 (The current URI for this page, for reference purposes)|
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