Сравнение методов

	Бутстреп-симуляция при наличии пропущенных данных	Метод Монте-Карло для данных с пропусками
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1979–1990s	1987–2002
Автор метода	Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others	Rubin, D. B. / Little, R. J. A.
Тип	Resampling simulation	Simulation-based estimation
Основополагающий источник	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. ISBN: 978-0412042317	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
Другие названия	bootstrap with missing data, bootstrap imputation simulation, resampling under missingness, bootstrap MI	MC simulation missing data, Monte Carlo imputation, simulation-based missing data analysis, stochastic simulation with incomplete data
Связанные	5	6
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.	Monte Carlo simulation with missing data combines stochastic simulation — drawing random values from probability distributions — with principled missing-data strategies such as multiple imputation. Instead of discarding incomplete records or substituting a single fill-in value, the method generates many simulated complete datasets, runs the target analysis on each, and pools the results to yield estimates that honestly reflect both sampling uncertainty and uncertainty due to missingness.