Methoden vergleichen

	Parametrischer Bootstrap	Bootstrap-Inferenz	Permutationstest (Randomisierungstest)
Fachgebiet	Statistik	Statistik	Statistik
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	1993	1979	2005
Urheber	Efron & Tibshirani; Davison & Hinkley	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
Typ	Resampling-based inference (model-based)	Resampling-based inference	Nonparametric resampling test
Wegweisende Quelle	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press. ISBN: 978-0412042317	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
Aliasnamen	parametrik bootstrap, model-based bootstrap, parametric resampling	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Verwandt	5	5	5
Zusammenfassung	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.	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.