改进了多重插补中缺失信息部分的估计方法。

IF 0.1 Q4 MATHEMATICS Cogent mathematics & statistics Pub Date : 2018-01-01 Epub Date: 2018-11-23 DOI:10.1080/25742558.2018.1551504

Qiyuan Pan, Rong Wei

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引用次数: 4

摘要

多重插补（MI）已成为处理缺失数据最流行的方法。与MI密切相关的是，缺失信息的分数（FMI）是诊断缺失数据影响的重要参数。目前，根据有限m的MI估计的FMI样本值γm被用作FMI总体值γ0的估计值，其中m是MI的输入次数。然而，这种FMI估计方法从未得到充分的证明和评估。在本文中，我们定量地证明了E（γm）随着m的增加而减小，因此对于任何有限的m，E（γm）>γ0。因此，γm不可避免地会高估γ0。提出了三种改进的FMI估计方法。MI试验的结果证实了主要结论，该试验使用了美国国家门诊医疗调查的2012年医师工作流程邮件调查的数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improved methods for estimating fraction of missing information in multiple imputation.

Multiple imputation (MI) has become the most popular approach in handling missing data. Closely associated with MI, the fraction of missing information (FMI) is an important parameter for diagnosing the impact of missing data. Currently γ _m , the sample value of FMI estimated from MI of a limited m, is used as the estimate of γ₀, the population value of FMI, where m is the number of imputations of the MI. This FMI estimation method, however, has never been adequately justified and evaluated. In this paper, we quantitatively demonstrated that E(γ _m ) decreases with the increase of m so that E(γ _m ) > γ₀ for any finite m. As a result γ _m would inevitably overestimate γ₀. Three improved FMI estimation methods were proposed. The major conclusions were substantiated by the results of the MI trials using the data of the 2012 Physician Workflow Mail Survey of the National Ambulatory Medical Care Survey, USA.