In practical survey sampling, missing data are unavoidable due to nonresponse, rejected observations by editing, disclosure control, or outlier suppression. We propose a calibrated imputation approach so that valid point and variance estimates of the population (or domain) totals can be computed by the secondary users using simple complete‐sample formulae. This is especially helpful for variance estimation, which generally require additional information and tools that are unavailable to the secondary users. Our approach is natural for continuous variables, where the estimation may be either based on reweighting or imputation, including possibly their outlier‐robust extensions. We also propose a multivariate procedure to accommodate the estimation of the covariance matrix between estimated population totals, which facilitates variance estimation of the ratios or differences among the estimated totals. We illustrate the proposed approach using simulation data in supplementary materials that are available online.

中文翻译：

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用于调查数据的二次数据分析的校准归因方法

在实际调查抽样中，由于没有响应，由于编辑，公开控制或离群值抑制而被拒绝的观察结果，不可避免地会丢失数据。我们提出了一种校准插补方法，以便二级用户可以使用简单的完整样本公式来计算总体（或域）总数的有效点和方差估计。这对于方差估计特别有用，因为方差估计通常需要辅助用户无法使用的其他信息和工具。对于连续变量，我们的方法很自然，因为估计可能基于重新加权或推算，包括可能的异常鲁棒扩展。我们还提出了一种多元程序，以适应估算的总体总数之间的协方差矩阵的估算，这有助于对估计总数之间的比率或差异进行方差估计。我们通过在线提供的补充材料中的仿真数据说明了所提出的方法。