手法を比較

	回帰推論のためのワイルドブートストラップ	ベイズブートストラップ（ルービン）	ブロックブートストラップ（移動ブロック法および定常法）	ブートストラップ推論
分野	統計学	統計学	統計学	統計学
系統	Regression model	Regression model	Regression model	Regression model
提唱年	1986	1981	1989	1979
提唱者	Wu (1986); refined by Davidson & Flachaire (2008)	Rubin (1981); large-sample theory by Lo (1987)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Bradley Efron
種類	Resampling-based regression inference	Resampling / posterior simulation	Resampling inference for dependent data	Resampling-based inference
原典	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	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.
別名	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	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
関連	5	5	5	5
概要	The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered.	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.