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首页> 外文期刊>Computational statistics & data analysis >A hot deck imputation procedure for multiply imputing nonignorable missing data: The proxy pattern-mixture hot deck
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A hot deck imputation procedure for multiply imputing nonignorable missing data: The proxy pattern-mixture hot deck

机译：多次插补不可忽略缺失数据的热插补插补程序：代理模式混合热插补

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摘要

Hot deck imputation is a common method for handling item nonresponse in surveys, but most implementations assume data are missing at random (MAR). A new hot deck method for imputation of a continuous partially missing outcome variable that harnesses the power of available covariates but does not assume data are MAR is proposed. A parametric model is used to create predicted means for both donors and donees under varying assumptions on the missing data mechanism, ranging from MAR to missing not at random (MNAR). For a given assumption on the missingness mechanism, the predicted means are used to define distances between donors and donees and probabilities of selection proportional to those distances. Multiple imputation using the hot deck is performed to create a set of completed data sets, using an approximate Bayesian bootstrap to ensure "proper" imputations. This new hot deck method creates an intuitive sensitivity analysis where imputations may be performed under MAR and under varying MNAR mechanisms, and the resulting impact on inference can be evaluated. In addition, a donor quality metric is proposed to help identify situations where close matches of donor to donee are not available, which can occur under strong MNAR assumptions. Bias and coverage of estimates from the proposed method are investigated through simulation and the method is applied to estimation of income in the Ohio Medicaid Assessment Survey. Results show that the method performs best when covariates are at least moderately predictive of the partially missing outcome, and without such covariates it effectively reduces to a simple random hot deck for all missingness assumptions. (C) 2014 Elsevier B.V. All rights reserved.
机译：热甲板插补是用于处理调查中项目无响应的一种常用方法，但是大多数实现都假设数据是随机（MAR）丢失的。提出了一种新的热插补方法，用于插补连续的部分缺失的结果变量，该方法利用了可用协变量的功效，但不假设数据是MAR。在缺失数据机制的各种假设下，从MAR到非随机缺失（MNAR），参数模型用于为捐赠者和受赠人创建预测均值。对于失踪机制的给定假设，使用预测的均值来定义施主和受赠人之间的距离以及与这些距离成正比的选择概率。使用近似的贝叶斯引导程序执行使用热平台的多次插补以创建一组完整的数据集，以确保“正确的”插补。这种新的热甲板方法创建了直观的灵敏度分析，可以在MAR和不同的MNAR机制下进行估算，并可以评估由此产生的对推理的影响。此外，提出了一个捐赠者质量度量标准，以帮助确定在强大的MNAR假设下可能发生的捐赠者与受赠人的紧密匹配的情况。通过模拟调查了所提出方法的偏差和估计范围，并将该方法应用于俄亥俄州医疗补助评估调查中的收入估计。结果表明，当协变量至少可以中等程度地预测部分缺失的结果时，该方法的效果最佳，并且在没有此类协变量的情况下，对于所有缺失假设，它都有效地简化为简单的随机热套牌。 （C）2014 Elsevier B.V.保留所有权利。

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