有条件的概率函数与不确定性估计估计
Marija Čargonja1, Domagoj Mateljak1, Boris Mifka1
1University of Rijeka, Faculty of Physics, Radmile Matejčić 2, Rijeka HR51000, Croatia.
The Science of the total environment
|April 5, 2025
概括
使用条件概率函数 (CPF) 和条件二变概率函数 (CBPF) 识别大气污染源的统计学意义往往被忽视. 这项研究引入了两种新方法,二项式比率和引导,以可靠地评估意义,提高大气研究的准确性.
科学领域:
- 大气化学和空气污染研究.
- 环境科学和数据分析.
- 环境研究中的统计建模.
背景情况:
- 条件概率函数 (CPF) 和条件二变概率函数 (CBPF) 对于识别大气污染源至关重要.
- 目前CPF和CBPF的应用往往缺乏统计学意义测试.
- 这种遗漏,加上小样本大小,可能导致关于污染源贡献的不准确结论.
研究的目的:
- 开发和验证统计学上合理的方法来评估CPF和CBPF结果的意义.
- 解决对确定大气污染源的可靠意义估计的关键需求.
- 提高从CPF和CBPF分析中得出的结论的准确性和可靠性.
主要方法:
- 开发了两个独立的统计方法:二项式比率和引导.
- 这些方法构建置信区间来估计CPF和CBPF的意义.
- 使用大型,现实世界的大气数据集验证的方法.
主要成果:
- 双项比率和引导方法在显著性估计方面表现出强烈的一致性.
- 经过验证的方法表现出良好的覆盖性质,这是信心区间可靠性的关键指标.
- 开发并发布开源R软件"CPFU"用于CPF,CBPF和信任区间计算和可视化.
结论:
- 开发的方法提供了一个统计学上可靠的方法来评估CPF和CBPF的显著性.
- 可靠的显著性测试提高了大气污染源识别的准确性.
- 自由可用的软件和数据提取工具支持更广泛的采用和改进的环境研究.
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