Porovnať metódy

	Parametrický bootstrap	BCa Bootstrap (Bias-Corrected and Accelerated)	Bootstrap inferencia	Test permutáciou (randomizačný test)
Odbor	Štatistika	Štatistika	Štatistika	Štatistika
Rodina	Regression model	Regression model	Regression model	Regression model
Rok vzniku	1993	1987	1979	2005
Tvorca	Efron & Tibshirani; Davison & Hinkley	Bradley Efron	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
Typ	Resampling-based inference (model-based)	Resampling confidence interval	Resampling-based inference	Nonparametric resampling test
Pôvodný zdroj	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
Ďalšie názvy	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
Príbuzné	5	5	5	5
Zhrnutie	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.