kpop:一种核心平衡方法,用于减少调查权重中的规范假设
Erin Hartman1, Chad Hazlett2,3, Ciara Sterbenz3
1Department of Political Science & Department of Statistics, University of California, Berkeley, USA.
概括
研究人员正在使用人口权重核平衡 (kpop) 来解决调查中的非代表性样本. 这种方法通过编码变量内的复杂关系来提高数据的准确性,克服了传统权重技术的局限性.
科学领域:
- 调查方法 调查方法
- 统计建模 统计建模
- 数据科学数据科学数据科学
背景情况:
- 调查响应率的下降导致了非代表性样本.
- 传统的权重方法依赖于关于变量关系的假设.
- 在变量选择方面的专家知识并不能保证准确的功能形式.
研究的目的:
- 引入种群权重 (kpop) 的内核平衡,作为传统校准权重的替代方案.
- 在调查权重中减轻功能形式的依赖.
- 在没有明确的功能形式规范的情况下提高样本数据的代表性.
主要方法:
- 将设计矩阵X替换为编码高阶信息的内核矩阵K.
- 计算权重以匹配样本中的核矩阵与人口的加权平均值.
- 使用核心平衡来对群体权重 (kpop) 进行可靠的校准.
主要成果:
- 内核平衡在X的广泛平滑函数上实现了良好的校准.
- 该kpop方法减少了对用户指定的功能形式的依赖.
- 使用2016年美国总统大选民意调查数据证明了有效性.
结论:
- 人口权重的核心平衡 (kpop) 为调查权重提供了更灵活和更强大的方法.
- 这种方法提高了非概率样本的代表性.
- 对于面临响应率下降的研究人员来说,kpop是一个有价值的工具.
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