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| Bootstrap Bayes (Rubin)× | BCa Bootstrap (Hiệu chỉnh thiên vị và Tăng tốc)× | Kiểm định hoán vị (Ngẫu nhiên hóa)× | |
|---|---|---|---|
| Lĩnh vực | Thống kê | Thống kê | Thống kê |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 1981 | 1987 | 2005 |
| Người khởi xướng≠ | Rubin (1981); large-sample theory by Lo (1987) | Bradley Efron | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Loại≠ | Resampling / posterior simulation | Resampling confidence interval | Nonparametric resampling test |
| Công trình gốc≠ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ | Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Tên gọi khác≠ | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Liên quan | 5 | 5 | 5 |
| Tóm tắt≠ | The Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated. | The BCa bootstrap is a resampling method, introduced by Bradley Efron in 1987, that produces more accurate confidence intervals than the plain percentile bootstrap by applying a bias correction and an acceleration adjustment. It is recommended for skewed distributions and small samples. | The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value. |
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