Calibrated Imputation in Surveys Under a Quasi-Model-Assisted Approach
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准模型辅助框架下调查数据的校准插补
- 研究问题
- 调查数据存在缺失时，如何通过校准插补估计域总量等线性总体参数，同时使推断不依赖插补模型的正确性并合理估计方差？
- 方法与思路
- 作者调整确定性单次插补所得的初始值，使其尽量接近原值并满足校准约束；推断结合抽样设计和非响应模型，方差采用泰勒线性化。
- 材料陈述的贡献
- 摘要称，适当约束下估计量渐近无偏；并在逻辑非响应模型下说明，忽略非响应参数估计的影响会高估插补估计量的方差。
- 阅读关注点
- 应核对校准约束如何保证渐近无偏、非响应模型承担哪些条件，以及多个插补方法并用时，方差估计如何计入参数估计的不确定性。
- 阅读边界
- 仅据所给 Crossref 摘要；不依赖插补模型正确性仍涉及抽样设计和非响应模型，具体条件与方差高估幅度未说明。
原文摘要
Summary We propose to use calibrated imputation to compensate for missing values. This technique consists of finding final imputed values that are as close as possible to preliminary imputed values and are calibrated to satisfy constraints. Preliminary imputed values, potentially justified by an imputation model, are obtained through deterministic single imputation. Using appropriate constraints, the resulting imputed estimator is asymptotically unbiased for estimation of linear population parameters such as domain totals. A quasi-model-assisted approach is considered in the sense that inferences do not depend on the validity of an imputation model and are made with respect to the sampling design and a non-response model. An imputation model may still be used to generate imputed values and thus to improve the efficiency of the imputed estimator. This approach has the characteristic of handling naturally the situation where more than one imputation method is used owing to missing values in the variables that are used to obtain imputed values. We use the Taylor linearization technique to obtain a variance estimator under a general non-response model. For the logistic non-response model, we show that ignoring the effect of estimating the non-response model parameters leads to overestimating the variance of the imputed estimator. In practice, the overestimation is expected to be moderate or even negligible, as shown in a simulation study.