方法对比

	贝叶斯自助法（Bayesian Bootstrap，由 Rubin 提出）	块自举（移动块和固定块）	Bootstrap Inference	普通最小二乘法 (OLS) 回归
领域	统计学	统计学	统计学	计量经济学
方法族	Regression model	Regression model	Regression model	Regression model
起源年份	1981	1989	1979	2019
提出者	Rubin (1981); large-sample theory by Lo (1987)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Bradley Efron	Wooldridge (textbook treatment); classical least squares
类型	Resampling / posterior simulation	Resampling inference for dependent data	Resampling-based inference	Linear regression
开创性文献	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
别名	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
相关	5	5	5	5
摘要	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.	Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994).	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).