방법 비교

	결측치가 있는 부트스트랩 시뮬레이션	Multiple Imputation
분야	베이지안	통계학
계열	Bayesian methods	Process / pipeline
기원 연도	1979–1990s	1987
창시자	Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others	Donald B. Rubin
유형	Resampling simulation	Missing-data handling procedure
원전	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. ISBN: 978-0412042317	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.
별칭	bootstrap with missing data, bootstrap imputation simulation, resampling under missingness, bootstrap MI	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)
관련	5	1
요약	Bootstrap simulation with missing data combines resampling-based variance estimation with principled handling of incomplete observations. Rather than deleting cases or assuming complete data, the method integrates imputation or weighting directly into the bootstrap loop, propagating the additional uncertainty due to missingness into the final standard errors and confidence intervals.	Multiple Imputation (MI), formally introduced by Donald B. Rubin in 1987, is a principled statistical procedure for handling missing data. Rather than replacing each missing value once, MI fills the gaps m times — each time drawing plausible values from the posterior predictive distribution of the missing data — producing m complete datasets. Each dataset is analysed independently, and the results are combined into a single set of estimates using Rubin's pooling rules. The MICE variant (Multivariate Imputation by Chained Equations), popularised by van Buuren and Groothuis-Oudshoorn (2011), extends the approach to mixed variable types by imputing each variable in turn through a sequence of conditional regression models.