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Divoký bootstrap pre regresnú inferenciu×Bayesovský bootstrap (Rubin)×
OdborŠtatistikaŠtatistika
RodinaRegression modelRegression model
Rok vzniku19861981
TvorcaWu (1986); refined by Davidson & Flachaire (2008)Rubin (1981); large-sample theory by Lo (1987)
TypResampling-based regression inferenceResampling / posterior simulation
Pôvodný zdrojWu, 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 ↗
Ďalšie názvywild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild BootstrapBayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap
Príbuzné55
ZhrnutieThe 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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ScholarGatePorovnať metódy: Wild Bootstrap · Bayesian Bootstrap. Získané 2026-06-15 z https://scholargate.app/sk/compare