Regression model
块自举（移动块和固定块）
块自举是一种用于处理依赖的、自相关的时序数据的重采样方法：它不是对单个观测值进行重采样，而是对连续观测值的整个块进行重采样，从而保留了序列相关结构。移动块变体由 Künsch (1989) 提出，固定块变体由 Politis 和 Romano (1994) 提出。
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Method map
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来源
- Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI: 10.1214/aos/1176347265 ↗
- Politis, D. N., & Romano, J. P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association, 89(428), 1303-1313. DOI: 10.1080/01621459.1994.10476870 ↗
如何引用本页
ScholarGate. (2026, June 1). Block Bootstrap (Moving Block and Stationary Bootstrap). ScholarGate. https://scholargate.app/zh/statistics/block-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.
- Bootstrap Inference统计学↔ compare
- Jackknife Resampling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
- 分位数回归计量经济学↔ compare