估计伯恩姆-桑德斯分布变异的估计方法含有零值,适用于泰国风速数据
Natchaya Ratasukharom1, Sa-Aat Niwitpong1, Suparat Niwitpong1
1Department of Applied Statistics, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
PeerJ
|October 21, 2024
概括
这项研究分析了风速数据,以了解泰国的空气污染. 它发现,使用不同的方法的通用信任信心区间 (GFCI) 是最好的估计风速变异在各种样本大小.
科学领域:
- 环境科学 环境科学
- 大气科学 大气科学
- 统计建模 统计建模
背景情况:
- 泰国面临严重的空气污染,特别是细颗粒物 (PM).
- 风速是大气污染物的大气分散的一个关键因素.
- 不能预测的风速模式需要强大的统计方法进行分析.
研究的目的:
- 为风速数据的变异建立置信区间 (CI).
- 评估分析空气质量背景下风速变化的统计方法.
- 确定在不同样本大小下估计风速偏差的最佳方法.
主要方法:
- 使用delta-Birnbaum-Saunders (delta-BirSau) 分布进行风速数据分析.
- 应用了通用的信任区间 (GCI),启动信任区间 (BCI) 和通用的信任信任区间 (GFCI) 方法.
- 纳入差异稳定转换 (VST),威尔逊和汉尼格方法用于零比例估计.
- 在R中进行蒙特卡洛模拟,以评估覆盖概率和间隔宽度.
主要成果:
- 一般化的信任信心区间 (GFCI) 在各种样本大小中表现出卓越的表现.
- 用威尔逊方法进行的GFCI对小样本尺寸来说是最佳的.
- 使用汉尼格方法的GFCI在中型样本大小方面表现出色.
- 使用VST方法的GFCI对于大样本尺寸是最佳的.
结论:
- 该研究为了解与空气污染相关的风速变化提供了一个统计框架.
- 一般化的信任区间可提供风速变异的可靠估计.
- 最佳的GFCI方法取决于样本大小,为泰国空气质量分析提供实用指南.
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