应用Covid-19修正frechet -指数分布的贝叶斯分析

AKDAM, Neriman
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引用次数: 0

摘要

本文讨论了修正frechet -指数分布参数的极大似然估计量和Bayes估计量。由于贝叶斯估计量不能以封闭的形式得到,所以在平方误差损失函数下,采用林德利近似法的思想计算近似贝叶斯估计量。然后,利用蒙特卡罗模拟,将近似贝叶斯估计与均方误差和偏差值方面的最大似然估计进行比较。最后,使用属于欧洲和中国的COVID-19死亡病例的真实数据集来证明属于近似贝叶斯估计的实证结果,即最大似然估计。
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Bayesian Analysis for the Modified Frechet–Exponential Distribution with Covid-19 Application
In this manuscript, the maximum likelihood estimators and Bayes estimators for the parameters of the modified Frechet–exponential distribution. Because the Bayes estimators cannot be obtained in closed forms, the approximate Bayes estimators are computed using the idea of Lindley’s approximation method under squared-error loss function. Then, the approximate Bayes estimates are compared with the maximum likelihood estimates in terms of mean square error and bias values using Monte Carlo simulation. Finally, real data sets belonging to COVID-19 death cases in Europe and China to are used to demonstrate the emprical results belonging to the approximate Bayes estimates, the maximum likelihood estimates.
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