Jackknife variance estimation for nearest-neighbor imputation
Jackknife variance estimation for nearest-neighbor imputation
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DOI：
10.1198/016214501750332839
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发表时间：
2001-03-01
影响因子：
3.7
通讯作者：
Shao, J
中科院分区：
文献类型：
--
作者：
Chen, JH;Shao, J
最近的邻居插补是一种流行的热甲板插补方法，用于补偿样品调查中的无响应。尽管该方法的应用历史悠久，但尚未完全研究最近邻居归因后的方差估计问题。由于最近的邻居插补是一种非参数方法，因此需要一种非参数方差估计技术，例如折刀。我们表明，视为观察到的数据会导致严重低估的幼稚折刀。我们还表明，Rao和Shao的调整后的折刀或每个伪变量重新模拟的折刀，这会产生渐近且一致的千斤顶和一致的折刀差异估计器（例如平均插补，随机的热甲板插图和比例插入和回归），在最近的邻居中，严重高估了。提出了两个部分重新模拟，并提出了部分调整后的折刀方差估计器，并证明是渐近公正和一致的。提供了一些经验结果来检查这些折刀方差估计器的有限样本特性。
Nearest-neighbor imputation is a popular hot deck imputation method used to compensate for nonresponse in sample surveys. Although this method has a long history of application, the problem of variance estimation after nearest-neighbor imputation has not been fully investigated. Because nearest-neighbor imputation is a nonparametric method, a nonparametric variance estimation technique, such as the jackknife, is desired. We show that the naive jackknife that treats imputed values as observed data produces serious underestimation. We also show that Rao and Shao's adjusted jackknife, or the jackknife with each pseudoreplicate reimputed, which produces asymptotically unbiased and consistent jackknife variance estimators for other imputation methods (such as mean imputation, random hot deck imputation, and ratio or regression imputation), produces serious overestimation in the case of nearest-neighbor imputation. Two partially reimputed and a partially adjusted jackknife variance estimators are proposed and shown to be asymptotically unbiased and consistent. Some empirical results are provided to examine finite-sample properties of these jackknife variance estimators.