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在MICE中用主要组件回归解决多变量问题

Edoardo Costantini¹, Kyle M Lang², Klaas Sijtsma³

¹Department of Methodology and Statistics, Tilburg University, Tilburg, Netherlands. e.costantini@tilburguniversity.edu.

Behavior research methods

|August 4, 2023

概括

主成分回归 (PCR) 通过在链式方程 (MICE) 多重推算 (Multiple Imputation by Chained Equations) 中自动化预测器选择,有效地处理大型数据集中缺失的数据. 这种方法的性能与社会科学研究中专家设计的程序相美.

科学领域:

社会科学社会科学社会科学
统计统计统计统计
数据科学数据科学数据科学

背景情况:

多重推算 (MI) 是处理调查和问卷中缺少数据的标准技术.
链式方程多变量推算 (MICE) 是一种灵活的MI方法,但需要仔细选择预测器,这在许多变量方面具有挑战性.

研究的目的:

研究主要组件回归 (PCR) 作为MICE中预测器选择的自动化方法,解决大型社会科学数据集中的"多变量"问题.
为了比较基于PCR的MICE实现与相关性值策略的性能.

主要方法:

探索主要组件回归 (PCR) 作为MICE框架内的单变量归算方法.
通过两个蒙特卡洛模拟研究和一个案例研究进行评估.
与相关性值预测因素选择策略进行比较.

主要成果:

在 MICE 内部按变量逐变量应用的 PCR 显示出卓越的性能.
基于PCR的MICE实现了与专家设计的归算程序相似的结果.
使用PCR的自动预测器选择有效地解决了大数据集中的挑战.

结论:

主要组件回归为MICE中自动预测器选择提供了强大而高效的解决方案,特别是对于大型社会科学数据.
基于PCR的MICE为手动预测器选择提供了可行的替代方案,提高了归算的实用性和性能.

关键词:

高维数据是高维数据.缺少的数据数据.多重的归咎是多重的归咎.主要组件回归的主要组成部分.

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