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	रिग्रेशन अनुमान के लिए वाइल्ड बूटस्ट्रैप	बेयसियन बूटस्ट्रैप (रूबिन)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	1986	1981
प्रवर्तक	Wu (1986); refined by Davidson & Flachaire (2008)	Rubin (1981); large-sample theory by Lo (1987)
प्रकार	Resampling-based regression inference	Resampling / posterior simulation
मौलिक स्रोत	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.
उपनाम	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap
संबंधित	5	5
सारांश	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.