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一个一般类的小面积估计使用校准的等级概率方法与应用到COVID-19数据的应用
Nirosha Rathnayake1, Hongying Daisy Dai1, Richard Charnigo2
1Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA.
Journal of applied statistics
|November 16, 2023
概括
本研究引入了对小面积估计 (SAE) 的校准层次 (CH) 概率方法,在没有大样本尺寸的情况下提高了准确性. 该方法有效地估计参数,避免复杂的集成,适用于各种分布.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 生物统计学 生物统计学
背景情况:
- 在小面积估计 (SAE) 中直接估计需要大样本大小.
- 现有的间接SAE模型 (例如,Fay-Herriot) 使用计算密集型方法,如边际概率和贝叶斯方法.
- 这些方法往往涉及难以整合的概率函数.
研究的目的:
- 为小面积估计提出一种新的校准分层 (CH) 概率方法.
- 开发一种方法,避免计算密集的集成.
- 增强SAE模型对更广泛的分布范围的适用性.
主要方法:
- 固定和随机效应的层次估计.
- 为偏差校正进行回归校准.
- 将潜在域级随机变量作为联合估计的参数.
- 使用拉普拉斯近似的概率概率的分散参数的代估计.
主要成果:
- 校准分层 (CH) 概率方法避免了边际分布估计的难以处理的整合.
- 拟议的方法适用于一般化的线性混合模型,生存分析和不同分布的联合建模.
- 通过对COVID-19阳性病例计数数据的区域级分析来证明有效性.
结论:
- CH概率方法为小面积估计提供了一个高效和灵活的替代方案.
- 这种方法提高了SAE模型的准确性和适用性,特别是在处理复杂的数据结构和分布时.
- 该方法为分析各种类型的数据提供了一个强大的框架,包括公共卫生监测数据.
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