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Analysis of Censored Data Using Dirichlet Process Mixture Model with Generalized Inverse Weibull Distribution as Kernel
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Bahram Haji Joudaki *    , Reza Hashemi , Soliman Khazaei |
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Abstract: (2264 Views) |
| In this paper, a new Dirichlet process mixture model with the generalized inverse Weibull distribution as the kernel is proposed. After determining the prior distribution of the parameters in the proposed model, Markov Chain Monte Carlo methods were applied to generate a sample from the posterior distribution of the parameters. The performance of the presented model is illustrated by analyzing real and simulated data sets, in which some data are right-censored. Another potential of the proposed model demonstrated for data clustering. Obtained results indicate the acceptable performance of the introduced model. |
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| Keywords: Mixture model, Dirichlet process, Generalized inverse Weibull distribution, Right censoring, Markov chain monte carlo. |
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Full-Text [PDF 454 kb]
(1614 Downloads)
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Type of Study: Applied |
Subject:
Statistical Inference
Received: 2023/04/23 | Accepted: 2024/02/29 | Published: 2024/02/22
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