Bayesian methodsBayesian / computational
带缺失数据蒙特卡洛模拟
带缺失数据的蒙特卡洛模拟结合了随机模拟——从概率分布中抽取随机值——以及诸如多重插补之类的原则性缺失数据策略。该方法不丢弃不完整的记录或用单个填充值替换，而是生成许多模拟的完整数据集，对每个数据集运行目标分析，并汇总结果以得出诚实反映抽样不确定性和缺失不确定性的估计值。
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来源
- Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
- van Buuren, S. (2018). Flexible Imputation of Missing Data (2nd ed.). CRC Press / Chapman & Hall. link ↗
如何引用本页
ScholarGate. (2026, June 3). Monte Carlo Simulation with Missing Data Handling. ScholarGate. https://scholargate.app/zh/bayesian/monte-carlo-simulation-with-missing-data
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 缺失数据的贝叶斯推断贝叶斯↔ compare
- 缺失数据时的自助法模拟贝叶斯↔ compare
- 带缺失数据的吉布斯抽样贝叶斯↔ compare
- 缺失数据下的MCMC贝叶斯↔ compare
- Multiple Imputation统计学↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare