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Divlji bootstrap za regresijsko zaključivanje×Block Bootstrap (Pokretni blok i stacionarni)×
PodručjeStatistikaStatistika
ObiteljRegression modelRegression model
Godina nastanka19861989
TvoracWu (1986); refined by Davidson & Flachaire (2008)Künsch (moving block, 1989); Politis & Romano (stationary, 1994)
VrstaResampling-based regression inferenceResampling inference for dependent data
Temeljni izvorWu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗
Drugi naziviwild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrapmoving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)
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.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).
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ScholarGateUsporedite metode: Wild Bootstrap · Block Bootstrap. Preuzeto 2026-06-15 s https://scholargate.app/hr/compare