Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment
Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment
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DOI：
10.1111/0272-4332.214136
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发表时间：
2001-08-01
期刊：
影响因子：
3.8
通讯作者：
Greenland, S
中科院分区：
文献类型：
--
作者：
Greenland, S
标准统计方法低估了从观测数据获得的效应估计所应具有的不确定性。用于解决此问题的方法包括敏感性分析、蒙特卡罗风险分析 (MCRA) 和贝叶斯不确定性评估。 MCRA 的估计已被视为有效的频率论或贝叶斯结果，但示例表明它们在实际应用中并不需要。结论是，敏感性分析和 MCRA 都应从与贝叶斯分析相同类型的事先规范工作开始。
Standard statistical methods understate the uncertainty one should attach to effect estimates obtained from observational data. Among the methods used to address this problem are sensitivity analysis, Monte Carlo risk analysis (MCRA), and Bayesian uncertainty assessment. Estimates from MCRAs have been presented as if they were valid frequentist or Bayesian results, but examples show that they need not be either in actual applications. It is concluded that both sensitivity analyses and MCRA should begin with the same type of prior specification effort as Bayesian analysis.