对于 GB2 分布的小面积估计的层次贝叶斯模型.
Binod Manandhar1, Balgobin Nandram2
1Department of Mathematical Sciences, Clark Atlanta University, Atlanta, GA, USA.
Journal of applied statistics
|October 6, 2025
概括
我们使用广义β分布 (GB2) 开发了贝叶斯模型,用于小面积估计. 这些模型通过结合调查和人口普查数据,准确估计贫困指标,改善对经济福祉的洞察力.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 贝叶斯的推理 贝叶斯的推理
背景情况:
- 在数据有限的地区,小面积估计对于决策至关重要.
- 第二种 (GB2) 分布的泛化β有效地模拟了偏斜的大小数据.
研究的目的:
- 开发和应用用于小面积估计的预测层次贝叶斯模型.
- 用连续的,正倾斜的大小数据来估计贫困指标.
主要方法:
- 在一个等级化的贝叶斯框架内利用了三种不同的GB2混合模型.
- 采用泰勒序列近似,网格采样和大都会采样器进行模型拟合.
- 将模型应用于来自尼泊尔生活标准调查的人均消费数据.
主要成果:
- 在三种拟议中确定了最合适的GB2混合物模型.
- 成功链接调查和人口普查数据,以提高小面积估计.
- 通过模拟研究证明了模型的有效性.
结论:
- 开发的贝叶斯式GB2模型为小面积估计提供了强大的框架.
- 通过整合调查和人口普查数据,可以准确估计贫困指标.
- 选择的模型为小地区的社会经济分析提供了有价值的见解.
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