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Showing 22 results for Subject:
Aqeel Lazam Razzaq, Isaac Almasi, Ghobad Saadat Kia, Volume 18, Issue 2 (2-2025)
Abstract
Adding parameters to a known distribution is a valuable way of constructing flexible families of distributions. In this paper, we introduce a new model, the modified additive hazard rate model, by replacing the additive hazard rate distribution in the general proportional add ratio model. Next, when two sets of random variables follow the modified additive hazard model, we establish stochastic comparisons between the series and parallel systems comprising these components.
Bahram Haji Joudaki, Soliman Khazaei, Reza Hashemi, Volume 19, Issue 1 (9-2025)
Abstract
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.
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