Item nonresponse is a common issue in surveys. Because unadjusted estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, commonly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents. In this article, we consider the class of multiply robust imputation procedures that provide some protection against the failure of underlying model assumptions. We develop an efficient version of multiply robust estimators based on the concept of conditional bias, a measure of influence. We present the results of a simulation study to show the benefits of our proposed method in terms of bias and efficiency.

中文翻译：

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在调查中存在有影响力的单位的情况下进行有效的乘法稳健插补

项目无响应是调查中的常见问题。由于未经调整的估计量在存在无响应的情况下可能会产生偏差，因此通常的做法是估算缺失值，以尽可能减少无响应偏差。然而，当受访者集中存在有影响力的单位时，常用的插补程序可能会导致人口总数/平均值的估计量不稳定。在本文中，我们考虑一类多重鲁棒插补程序，它提供了一些保护，防止基础模型假设的失败。我们基于条件偏差（影响力的衡量标准）的概念开发了一个高效版本的乘法鲁棒估计器。我们提出了模拟研究的结果，以展示我们提出的方法在偏差和效率方面的优势。