通过统计建模探索贫困:双变多项二进制逻辑回归 (BPBLR)
Vita Ratnasari1, Purhadi1, Marisa Rifada1
1Sepuluh Nopember Institute of Technology, Airlangga University, Mulawarman University, Indonesia.
MethodsX
|January 15, 2025
概括
我们介绍了双变多项二进制逻辑回归 (BPBLR) 用于分析两个相关的二进制结果. 这种统计方法增强了对复杂的分类数据分析的逻辑回归,有助于评估贫困.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 社会科学 社会科学 社会科学
背景情况:
- 逻辑回归是一种用于分析分类数据,特别是二进制响应的标准统计方法.
- 现有的模型往往难以有效地捕捉多个二进制结果变量之间的相关性.
研究的目的:
- 为了引入双变多项二进制逻辑回归 (BPBLR) 模型.
- 使用多项式模式扩展逻辑回归来建模两个相关的二进制响应变量.
- 将BPBLR模型应用于可持续发展目标 (SDGs) 的现实世界贫困数据 1.
主要方法:
- 提出了双变多项二进制逻辑回归 (BPBLR) 模型,它结合了多项式模式来描述相关的二进制响应和预测变量之间的关联.
- 参数估计是使用最大概率估计 (MLE) 方法进行的.
- 该模型的统计测试是使用最大概率比率测试 (MLRT) 进行的,测试统计数据以非对称的方式遵循基平方分布.
- 模型选择和最佳多项式度的确定是基于最小化偏差值.
主要成果:
- 该BPBLR模型提供了一种新的方法,用于对类别数据的统计建模,其中有两个相关的二进制响应变量.
- 使用MLRT方法对拟议模型进行可靠的统计测试.
- 对贫困数据集的应用证明了该模型在分析贫困的深度和严重程度方面的实用性,有助于实现可持续发展目标1的目标.
结论:
- 该BPBLR模型为处理相关的二进制结果提供了一个重要的统计建模创新.
- 开发的方法,包括MLE和MLRT,为分析和测试提供了一个全面的框架.
- 对贫困数据的成功应用凸显了该模型在解决复杂的社会问题和支持全球发展目标方面的实际相关性.
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