Resampling Methods with Imputed Data
arxiv(2023)
摘要
Resampling techniques have become increasingly popular for estimation of
uncertainty in data collected via surveys. Survey data are also frequently
subject to missing data which are often imputed. This note addresses the issue
of using resampling methods such as a jackknife or bootstrap in conjunction
with imputations that have be sampled stochastically (e.g., in the vein of
multiple imputation). It is illustrated that the imputations must be redrawn
within each replicate group of a jackknife or bootstrap. Further, the number of
multiply imputed datasets per replicate group must dramatically exceed the
number of replicate groups for a jackknife. However, this is not the case in a
bootstrap approach. A brief simulation study is provided to support the theory
introduced in this note.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要