Modeling error distribution in accelerated failure time models using a  Dirichlet processes mixture model with the Burr XII distribution as the kernel

Haji joudaki, bahram; Khazaei, Soliman; Hashemi, reza

doi:10.61186/jss.19.1.7

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Modeling error distribution in accelerated failure time models using a Dirichlet processes mixture model with the Burr XII distribution as the kernel

Bahram Haji joudaki

, Soliman Khazaei ^*

, Reza Hashemi

Abstract: (284 Views)

Accelerated failure time models are used in survival analysis when the data is censored, especially when combined with auxiliary variables. When the models in question depend on an unknown parameter, one of the methods that can be applied is Bayesian methods, which consider the parameter space as infinitely dimensional. In this framework, the Dirichlet process mixture model plays an important role. In this paper, a Dirichlet process mixture model with the Burr XII distribution as the kernel is considered for modeling the survival distribution in the accelerated failure time. Then, MCMC methods were employed to generate samples from the posterior distribution. The performance of the proposed model is compared with the Polya tree mixture models based on simulated and real data. The results obtained show that the proposed model performs better.

Keywords: Mixture Model, Accelerated Failure Time Model, Dirichlet Process, Burr XII Distribution, Censored Data, MCMC.

Full-Text [PDF 2546 kb] (153 Downloads)

Type of Study: Applied | Subject: Statistical Inference
Received: 2024/09/19 | Accepted: 2024/08/31

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