Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Бутстреп-симуляція з пропущеними даними× | Метод Монте-Карло для даних з пропусками× | |
|---|---|---|
| Галузь | Баєсові методи | Баєсові методи |
| Родина | Bayesian methods | Bayesian methods |
| Рік появи≠ | 1979–1990s | 1987–2002 |
| Автор методу≠ | 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 |
| Основоположне джерело≠ | 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 |
| Інші назви | 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 |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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