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Divlji bootstrap za regresijsko zaključivanje×Bayesov bootstrap (Rubin)×
PodručjeStatistikaStatistika
ObiteljRegression modelRegression model
Godina nastanka19861981
TvoracWu (1986); refined by Davidson & Flachaire (2008)Rubin (1981); large-sample theory by Lo (1987)
VrstaResampling-based regression inferenceResampling / posterior simulation
Temeljni izvorWu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗
Drugi naziviwild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild BootstrapBayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap
Srodne55
SažetakThe 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.
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ScholarGateUsporedite metode: Wild Bootstrap · Bayesian Bootstrap. Preuzeto 2026-06-15 s https://scholargate.app/hr/compare