Сравнение на методи

	Див бутстрап за регресионно заключение	Байесовски бутстрап (Рубин)	Бутстрап извод
Област	Статистика	Статистика	Статистика
Семейство	Regression model	Regression model	Regression model
Година на възникване	1986	1981	1979
Създател	Wu (1986); refined by Davidson & Flachaire (2008)	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron
Тип	Resampling-based regression inference	Resampling / posterior simulation	Resampling-based inference
Основополагащ източник	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.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
Други названия	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Свързани	5	5	5
Резюме	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.	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.