MULTIPLE-IMPUTATION INFERENCES WITH UNCONGENIAL SOURCES OF INPUT
MULTIPLE-IMPUTATION INFERENCES WITH UNCONGENIAL SOURCES OF INPUT
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DOI：
10.1214/ss/1177010269
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发表时间：
1994-11-01
影响因子：
5.7
通讯作者：
MENG, XL
中科院分区：
文献类型：
--
作者：
MENG, XL
进行抽样调查，估算不完整的观察结果，并分析所产生的数据是现代实践的三个不可或缺的阶段与公用数据文件和许多其他统计应用。每个阶段都继承不同的投入，包括之前的信息和现有的知识评估，目的是提供更接近于得出具有科学相关性的统计推断的产出。然而，插补阶段的作用往往被视为仅仅为数据用户提供计算便利。虽然方便计算非常重要，但这种观点忽视了估算者的评估和用户无法获得的信息。这种观点是最近关于多重插补推断有效性的争议的基础，因为分析多重插补数据集的程序不能从多重插补所采用的模型中推导出来（与多重插补所采用的模型不一致）。考虑到合理的插补和完整的数据分析程序，从标准的多重插补组合作用的推论通常是上级，因此不同于，用户的不完整的数据分析。后者可能遭受严重的无反应偏见，因为这样的分析往往必须依赖于方便，但不切实际的假设无反应机制。当需要在原始插补模型以外的模型下进行无应答推断时，重新创建插补的一种可能替代方法是将适当的重要性权重纳入标准合并规则。这些观点通过简单的例子和一般理论进行了回顾和探索，从贝叶斯和频率论的角度，特别是从随机化的角度。在评价具有不同输入源的多重插补推理时，为了便于不同研究者之间的交流，提出了一些方便的术语。
Conducting sample surveys, imputing incomplete observations, and analyzing the resulting data are three indispensable phases of modern practice with public-use data files and with many other statistical applications. Each phase inherits different input, including the information preceding it and the intellectual assessments available, and aims to provide output that is one step closer to arriving at statistical inferences with scientific relevance. However, the role of the imputation phase has often been viewed as merely providing computational convenience for users of data. Although facilitating computation is very important, such a viewpoint ignores the imputer's assessments and information inaccessible to the users. This view underlies the recent controversy over the validity of multiple-imputation inference when a procedure for analyzing multiply imputed data sets cannot be derived from (is ''uncongenial'' to) the model adopted for multiple imputation. Given sensible imputations and complete-data analysis procedures, inferences from standard multiple-imputation combining roles are typically superior to, and thus different from, users' incomplete-data analyses. The latter may suffer from serious nonresponse biases because such analyses often must rely on convenient but unrealistic assumptions about the nonresponse mechanism. When it is desirable to conduct inferences under models for nonresponse other than the original imputation model, a possible alternative to recreating imputations is to incorporate appropriate importance weights into the standard combining rules. These points are reviewed and explored by simple examples and general theory, from both Bayesian and frequentist perspectives, particularly from the randomization perspective. Some convenient terms are suggested for facilitating communication among researchers from different perspectives when evaluating multiple-imputation inferences with uncongenial sources of input.