Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Simulación Monte Carlo con Datos Faltantes× | Simulación Bootstrap con Datos Faltantes× | |
|---|---|---|
| Campo | Bayesiano | Bayesiano |
| Familia | Bayesian methods | Bayesian methods |
| Año de origen≠ | 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 |
| Fuente 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 |
| Alias | 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 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
|
|