Process / pipelineMissing data
缺失数据机制：MCAR、MAR与MNAR
缺失数据机制由Donald Rubin于1976年引入，为分类数据集中观测值缺失的原因提供了一个形式化分类。三个类别——完全随机缺失（MCAR）、随机缺失（MAR）和非随机缺失（MNAR）——描述了缺失概率与已观测或未观测值之间的关系。识别正确的机制至关重要，因为它决定了哪些分析策略能够保持有效且无偏的推断。
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来源
- Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592. DOI: 10.1093/biomet/63.3.581 ↗
如何引用本页
ScholarGate. (2026, June 2). Missing Data Mechanisms (MCAR, MAR, MNAR). ScholarGate. https://scholargate.app/zh/statistics/missing-data-mechanisms
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