Comparar métodos

	Simulação de Monte Carlo com Dados Ausentes	Simulação Bootstrap com Dados Faltantes
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	1987–2002	1979–1990s
Autor original	Rubin, D. B. / Little, R. J. A.	Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others
Tipo	Simulation-based estimation	Resampling simulation
Fonte seminal	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. ISBN: 978-0412042317
Outros nomes	MC simulation missing data, Monte Carlo imputation, simulation-based missing data analysis, stochastic simulation with incomplete data	bootstrap with missing data, bootstrap imputation simulation, resampling under missingness, bootstrap MI
Relacionados	6	5
Resumo	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.	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.