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Updated: Jul 23, 2026

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
Published on: July 22, 2016
A semi-parametric Bayesian analysis of survival data based on Lévy-driven processes
Luis E Nieto-Barajas1, Stephen G Walker
1ITAM, México DF, México. lnieto@itam.mx
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 (Lévy-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.
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