Comparar métodos

	Simulação Bootstrap com Dados Faltantes	Monte Carlo Sequencial com Dados Ausentes
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	1979–1990s	1993–2001
Autor original	Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others	Gordon, Salmond & Smith (particle filter, 1993); missing-data extensions formalised by Doucet et al. (2000s)
Tipo	Resampling simulation	Sequential Bayesian filtering / smoothing
Fonte seminal	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC. ISBN: 978-0412042317	Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York. ISBN: 978-0387951461
Outros nomes	bootstrap with missing data, bootstrap imputation simulation, resampling under missingness, bootstrap MI	SMC with missing data, particle filter with missing observations, SMC missing observations, particle smoothing with incomplete data
Relacionados	5	6
Resumo	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.	Sequential Monte Carlo (SMC) with missing data extends the standard particle filter to state-space models in which some observations are absent. When an observation is missing at a given time step the update step is simply skipped: particles are propagated forward through the transition model without reweighting, preserving exact Bayesian inference under any missing-data pattern as long as missingness is ignorable (missing at random or missing completely at random).