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BCa 부트스트랩 (편향 보정 및 가속)×회귀 추론을 위한 와일드 부트스트랩×
분야통계학통계학
계열Regression modelRegression model
기원 연도19871986
창시자Bradley EfronWu (1986); refined by Davidson & Flachaire (2008)
유형Resampling confidence intervalResampling-based regression inference
원전Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI ↗Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗
별칭BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence intervalwild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap
관련55
요약The BCa bootstrap is a resampling method, introduced by Bradley Efron in 1987, that produces more accurate confidence intervals than the plain percentile bootstrap by applying a bias correction and an acceleration adjustment. It is recommended for skewed distributions and small samples.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.
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