基于带有协变量的Cure模型的Beta Kumaraswamy Burr型X (Beta Kum-BX)分布的贝叶斯参数估计

U. Madaki, Mohd. Rizam Abu Bakar
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引用次数: 0

摘要

在剔除生存数据的统计模型中,包括一部分不受研究中感兴趣事件影响的个体,被称为长期生存治愈模型。在估计固化分数时,有两种最常用的模型,即混合模型(标准固化)和非混合模型。在本研究中,我们引入了一种贝叶斯方法,使用基于6个参数的β Kumaraswamy Burr X型分布的两种模型来分析生存数据,并与现有的两种模型(β - weibull和β -广义指数分布)进行了比较。所提出的方法允许在模型中包含协变量。采用极大似然分析和贝叶斯分析方法进行参数估计。另一方面,Gibbs抽样算法采用R软件中的win Bugs和MCMC包库,通过应用真实数据集获得兴趣的后验摘要和跟踪图,并基于cure模型进行了仿真研究,比较了两种模型在实际动机感和新颖性方面的性能,从而阐明了所提出方法的实用性。
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A Bayesian Parametric Estimation of Beta Kumaraswamy Burr Type X (Beta Kum-BX) Distribution Based on Cure Models with Covariates
In statistical models for censored survival data which includes a proportion of individuals who are not subject to the event of interest under study are known as the long-term survival cured models. It has two most adopted and common models used in estimating the cure fraction namely: the mixture (standard cure) and the non-mixture models. In this research work, we introduce a Bayesian approach using the two models for survival data based on the Beta Kumaraswamy Burr Type X distribution with six parameters and compared with two existing models: beta-Weibull and beta-generalized exponential distributions in analyzing a real-life dataset. The proposed approach allows the inclusion of covariates in the model. The parameter estimation was obtained by maximum likelihood and Bayesian analysis methods. The win Bugs and MCMC pack library in R softwares were employed for the Gibbs sampling algorithm in other to obtain the posterior summaries of interest and also the trace plots by the applying of real data sets and a simulation study was done based on cure models to compare the performance of both models relating to actual sense of motivation and novelty which clarifies the usefulness of the proposed methodologies.
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来源期刊
Songklanakarin Journal of Science and Technology
Songklanakarin Journal of Science and Technology Multidisciplinary-Multidisciplinary
CiteScore
1.10
自引率
0.00%
发文量
0
审稿时长
25 weeks
期刊介绍: Songklanakarin Journal of Science and Technology (SJST) aims to provide an interdisciplinary platform for the dissemination of current knowledge and advances in science and technology. Areas covered include Agricultural and Biological Sciences, Biotechnology and Agro-Industry, Chemistry and Pharmaceutical Sciences, Engineering and Industrial Research, Environmental and Natural Resources, and Physical Sciences and Mathematics. Songklanakarin Journal of Science and Technology publishes original research work, either as full length articles or as short communications, technical articles, and review articles.
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