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| Mô phỏng Monte Carlo với dữ liệu thiếu× | MCMC với dữ liệu thiếu× | |
|---|---|---|
| Lĩnh vực | Bayes | Bayes |
| Họ | Bayesian methods | Bayesian methods |
| Năm ra đời≠ | 1987–2002 | 1987 |
| Người khởi xướng≠ | Rubin, D. B. / Little, R. J. A. | Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin |
| Loại≠ | Simulation-based estimation | Bayesian computational method |
| Công trình gốc | Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860 | Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860 |
| Tên gọi khác | MC simulation missing data, Monte Carlo imputation, simulation-based missing data analysis, stochastic simulation with incomplete data | MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | 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. | MCMC with missing data is a Bayesian computational strategy that treats unobserved values as additional unknown parameters. By alternating between sampling the missing values from their predictive distribution and sampling the model parameters from their posterior, the algorithm produces a valid joint posterior that fully accounts for uncertainty introduced by the missingness. |
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