方法对比
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| 自助法模拟× | 刀切法估计× | |
|---|---|---|
| 领域≠ | 仿真 | 统计学 |
| 方法族≠ | Process / pipeline | Hypothesis test |
| 起源年份≠ | 1979 | 1956 |
| 提出者≠ | Bradley Efron | Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming) |
| 类型≠ | Simulation-based nonparametric inference | Bias and variance estimation |
| 开创性文献≠ | Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC. DOI ↗ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353–360. DOI ↗ |
| 别名≠ | bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling) | delete-one jackknife, leave-one-out jackknife, Jackknife Yeniden Örnekleme |
| 相关≠ | 5 | 3 |
| 摘要≠ | Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data. | Jackknife estimation is a classical resampling technique that computes the bias and variance of a statistical estimator by systematically leaving out one observation at a time and re-computing the statistic on each reduced sample. Introduced by Maurice Quenouille in 1956 for bias correction and extended by John Tukey in 1958 who coined the name, it is the historical predecessor of the bootstrap and remains analytically tractable for smooth, differentiable estimators. |
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