Regression model
Wild Bootstrap for Regression Inference
Wild bootstrap 是一种用于处理异方差误差的回归模型重采样方法，由 Wu (1986) 提出并由 Davidson 和 Flachaire (2008) 改进。它通过对每个拟合残差随机乘以一个随机符号来构建 bootstrap 分布，从而在误差方差不恒定或数据存在聚类时，标准误和置信区间仍然有效。
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来源
- Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI: 10.1214/aos/1176350142 ↗
- Davidson, R., & Flachaire, E. (2008). The Wild Bootstrap, Tamed at Last. Journal of Econometrics, 146(1), 162-169. DOI: 10.1016/j.jeconom.2008.08.003 ↗
如何引用本页
ScholarGate. (2026, June 1). Wild Bootstrap for Regression Inference. ScholarGate. https://scholargate.app/zh/statistics/wild-bootstrap
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.
- 贝叶斯自助法（Bayesian Bootstrap，由 Rubin 提出）统计学↔ compare
- 块自举（移动块和固定块）统计学↔ compare
- Bootstrap Inference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