Compara mètodes

	Bootstrap paramètric	Bootstrap BCa (corregit de biaix i accelerat)	Inferencia Bootstrap	Test de permutació (aleatorització)
Camp	Estadística	Estadística	Estadística	Estadística
Família	Regression model	Regression model	Regression model	Regression model
Any d'origen	1993	1987	1979	2005
Autor original	Efron & Tibshirani; Davison & Hinkley	Bradley Efron	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
Tipus	Resampling-based inference (model-based)	Resampling confidence interval	Resampling-based inference	Nonparametric resampling test
Font seminal	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press. ISBN: 978-0412042317	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Àlies	parametrik bootstrap, model-based bootstrap, parametric resampling	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Relacionats	5	5	5	5
Resum	The parametric bootstrap is a resampling method that estimates standard errors and confidence intervals by drawing repeated samples from a parametric model that has been fitted to the data. Developed in the bootstrap literature of Efron and Tibshirani (1993) and Davison and Hinkley (1997), it replaces analytic derivations for non-normal distributions and complex statistics.	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.	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.