对于几个林德利人群的点估计和相关分类问题,应用程序使用COVID-19数据.
Debasmita Bal1, Manas Ranjan Tripathy1, Somesh Kumar2
1Department of Mathematics, National Institute of Technology Rourkela, Rourkela, Odisha, India.
Journal of applied statistics
|July 29, 2024
概括
本研究引入了使用马尔科夫链蒙特卡洛 (MCMC) 和Tierney和Kadane的林德利分布的改进的贝叶斯估计器.
科学领域:
- 统计建模 统计建模
- 贝叶斯的推理 贝叶斯的推理
- 机器学习 机器学习
背景情况:
- 林德利分布是各种数据类型的灵活模型.
- 准确的参数估计和分类在统计分析中至关重要.
研究的目的:
- 为林德利分布参数开发和评估新的贝叶斯估计器.
- 根据这些估计器提出和评估分类规则.
- 将这些方法应用于现实世界的COVID-19数据.
主要方法:
- 使用马尔科夫链蒙特卡洛 (MCMC) 和Tierney和Kadane的方法推导贝叶斯估计器.
- 贝叶斯估计器对最大概率估计器 (MLE) 的收分析.
- 使用偏差和平均平方误差 (MSE) 的估计器的数值比较.
- 制定分类规则,包括概率合规函数规则.
- 使用预期错误分类概率 (EPM) 评估分类规则.
主要成果:
- 拟议的贝叶斯估计器在模拟研究中表现出高于现有方法的性能.
- 随着样本大小的增加,贝叶斯估计器汇聚到MLE.
- 分类规则在数值评估中显示了有效的表现.
结论:
- 开发的贝叶斯估计器和分类规则为林德利分布式数据提供了更高的准确性.
- 该方法适用于现实世界的问题,例如分析COVID-19数据.
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