Regression model
Jackknife Resampling
Jackknife是一种经典的重采样方法，它通过系统地一次剔除一个观测值来重新计算统计量，从而估计统计量的偏差和方差。该方法由Quenouille于1956年提出，后由Miller于1974年进行回顾，它早于bootstrap方法，至今仍是评估估计量稳定性的简单、确定性工具。
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Method map
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来源
- Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI: 10.1093/biomet/43.3-4.353 ↗
- Miller, R. G. (1974). The Jackknife — A Review. Biometrika, 61(1), 1-15. DOI: 10.1093/biomet/61.1.1 ↗
如何引用本页
ScholarGate. (2026, June 1). Jackknife Resampling. ScholarGate. https://scholargate.app/zh/statistics/jackknife
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Bootstrap Inference统计学↔ compare
- 中位数绝对离差 (MAD) 估计统计学↔ compare
- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- 置换 (随机化) 检验统计学↔ compare
- 稳健时间序列分析统计学↔ compare