Сравнение методов

	Бутстреп-симуляция при наличии пропущенных данных	Множественная импутация
Область	Байесовские методы	Статистика
Семейство	Bayesian methods	Process / pipeline
Год появления	1979–1990s	1987
Автор метода	Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others	Donald B. Rubin
Тип	Resampling simulation	Missing-data handling procedure
Основополагающий источник	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.
Другие названия	bootstrap with missing data, bootstrap imputation simulation, resampling under missingness, bootstrap MI	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)
Связанные	5	1
Сводка	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.