Bayesian nonparametric survival analysis using mixture of Burr XII distributions

Recently, the Bayesian nonparametric approaches in survival studies attract much more attentions. Because of multimodality in survival data, the mixture models are very common. We introduce a Bayesian nonparametric mixture model with Burr distribution (Burr type XII) as the kernel. Since the Burr di...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2018-10, Vol.47 (9), p.2724-2738
Main Authors: Bohlouri Hajjar, S., Khazaei, S.
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian nonparametric
Burr XII distribution
Computer simulation
Dirichlet problem
Dirichlet process
Failure times
Markov analysis
Markov chains
Monte Carlo simulation
Nonparametric statistics
Right censored data
Survival
Survival analysis
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Summary:Recently, the Bayesian nonparametric approaches in survival studies attract much more attentions. Because of multimodality in survival data, the mixture models are very common. We introduce a Bayesian nonparametric mixture model with Burr distribution (Burr type XII) as the kernel. Since the Burr distribution shares good properties of common distributions on survival analysis, it has more flexibility than other distributions. By applying this model to simulated and real failure time datasets, we show the preference of this model and compare it with Dirichlet process mixture models with different kernels. The Markov chain Monte Carlo (MCMC) simulation methods to calculate the posterior distribution are used.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2017.1359286