Bandingkan metode

	Bootstrap BCa (Bias-Corrected and Accelerated)	Bootstrap Bayesian (Rubin)
Bidang	Statistika	Statistika
Keluarga	Regression model	Regression model
Tahun asal	1987	1981
Pencetus	Bradley Efron	Rubin (1981); large-sample theory by Lo (1987)
Tipe	Resampling confidence interval	Resampling / posterior simulation
Sumber perintis	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.
Alias	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap
Terkait	5	5
Ringkasan	The 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 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.