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首页> 外文期刊>Asian Journal of Scientific Research >Performance of Bayesian Using Conjugate Prior Estimator for Weibull Right Censored Survival Data
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Performance of Bayesian Using Conjugate Prior Estimator for Weibull Right Censored Survival Data

机译：贝叶斯使用共轭先验估计器对威布尔右删失生存数据的性能

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Background and Objective: The Weibull distribution is widely used to model and to analyze data on the survival time. Bayesian estimation approach has received much attention as it has been in contention with other estimation methods. In this study, it was examined the performance of the Bayesian estimator using conjugate prior information for estimating the parameters of Weibull distribution with censored survival data for dengue fever (DF). Materials and Methods: Through the simulated Weibull distributed survival dataset, the performance of conjugate estimator for estimating the Weibull distribution parameters can be checked before applying it to the DF survival dataset in Makassar, Indonesia. Statistical analysis of the simulation data and collected DF data were analyzed through summary tables and Markov Chain Monte Carlo method via Gibbs sampling algorithm. It was performed using R version 3.3.3 and Win BUGS. Results: Based on the simulation study, the mean of posterior means of all Weibull distribution parameter estimates are still reasonably accurate. After fitting the Weibull models to the DF survival time?s dataset using the conjugate prior distribution, the age factor substantially described DF patients? survival times and had a positive effect on the estimated survival time. Conclusion: To choose sample size and censoring level, the estimates generated by the conjugate prior do not only depend on the data but also on the parameters of the prior distribution. The amount of uncensored data must be more than one in order to obtain an estimate of greater than zero. The results of estimated parameters of Weibull model using conjugate prior either with simulated survival data or the DF data is good.
机译：背景与目的：Weibull分布广泛用于建模和分析生存时间数据。贝叶斯估计方法已经引起了人们的广泛关注，因为它一直与其他估计方法有争议。在这项研究中，我们使用共轭先验信息评估了贝叶斯估计器的性能，该信息用于评估带有登革热（DF）的经过审查的生存数据的威布尔分布参数。材料和方法：通过模拟的Weibull分布式生存数据集，可以将共轭估计器用于估计Weibull分布参数的性能，然后再将其应用于印度尼西亚望加锡的DF生存数据集。通过汇总表和基于Gibbs采样算法的Markov Chain Monte Carlo方法对仿真数据和收集的DF数据进行统计分析。它是使用R版本3.3.3和Win BUGS执行的。结果：根据仿真研究，所有威布尔分布参数估计的后均值均值仍然相当准确。使用共轭先验分布将Weibull模型拟合为DF生存时间的数据集后，年龄因素基本描述了DF患者？生存时间，对估计生存时间有积极影响。结论：选择样本量和审查水平，共轭先验产生的估计不仅取决于数据，而且还取决于先验分布的参数。未经审查的数据量必须大于一个，以便获得大于零的估计值。使用共轭先验或模拟生存数据或DF数据对Weibull模型的参数估计结果良好。

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