完全合成的数据用于复杂的调查
Shirley Mathur1, Yajuan Si2, Jerome P Reiter3
1Department of Statistics, B-313 Padelford Hall, University of Washington, Seattle, WA 98195-4322.
概括
统计机构可以从复杂的调查设计中创建完全合成的数据. 这种方法使用加权的贝叶斯式启动和多重归咎来强有力的公开发布机密信息.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 调查方法 调查方法
背景情况:
- 发布保密调查数据供公众使用是一项挑战.
- 统计机构需要方法来生成保护隐私的合成数据.
- 现有的方法可能无法完全解释复杂的调查设计.
研究的目的:
- 提出一种新的方法,从复杂的抽样设计的调查中生成完全合成的数据.
- 确保合成数据准确地反映了原始数据的分析属性.
- 为了方便使用合成数据进行分析的方差估计.
主要方法:
- 使用加权有限人口贝叶斯式引导来处理调查权重生成伪人口.
- 从伪人口中抽取简单的随机样本来估计合成模型.
- 采用两种数据生成策略的多重归算来进行差异估计.
- 为每个策略制定和呈现多重归算结合规则.
主要成果:
- 拟议的方法从复杂的调查中生成完全合成的数据,适合公开发布.
- 模拟研究证明了多重归算结合规则的重复采样特性.
- 对比显示了有利的结果与基于伪概率方法的合成数据生成.
- 这种方法成功地应用于美国社区调查数据.
结论:
- 拟议的方法为创建完全合成的调查数据提供了一个统计学上合理的方法.
- 这有助于更广泛地传播有价值的数据,同时保护受访者保密性.
- 该方法强大,可适应各种复杂的调查设计.
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