使用多次归算数据的重新采样方法
Michael W Robbins1, Lane Burgette2
1RAND Corporation, 4570 Fifth Avenue #600, Pittsburgh, Pennsylvania 15213, USA.
Biometrika
|November 17, 2025
概括
这项研究阐明了使用重新采样方法,如用随机归纳的jackknife和bootstrap. 它表明,归算必须在复制体内独立,数据集的数量因重新采样类型而异,以准确地估计不确定性.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 计算统计学 计算统计学
背景情况:
- 重复采样技术 (大刀,引导) 对于不确定性估计至关重要.
- 缺失的数据很常见,通常通过归算处理,包括多重归算.
- 将重新抽样与随机归纳结合起来,会带来独特的理论挑战.
研究的目的:
- 为了推导出使用卡刀和靴带与随机归算的理论.
- 为正确实施这些方法提供指导.
- 为了解决复制组和归算数据集之间的相互作用.
主要方法:
- 对于随机归算的重新采样规则的理论推导.
- 在多重归算下分析刀和引导式方法.
- 讨论偏差调整的刀和引导方法.
- 模拟研究以验证理论发现.
主要成果:
- 在每个复制组内,必须独立地为jackknife和bootstrap生成输入.
- 杰克刀需要比复制组要多得多的归算数据集.
- 对于相对于复制组的归算数据集的数量,Bootstrap没有同样严格的要求.
- 偏差调整的方法可以减少对众多归算数据集的需求.
结论:
- 使用随机归算重新采样的正确应用需要仔细考虑归算生成和数据集数量.
- 该理论框架阐明了在不完整的数据集中估计不确定性的最佳策略.
- 研究结果为使用这些先进的统计方法的研究人员提供了实际指导.
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