Módszerek összehasonlítása

	Bootstrap-becslés	Medián Abszolút Deviáció (MAD) Becslés	Permutációs (randomizációs) teszt
Tudományterület	Statisztika	Statisztika	Statisztika
Módszercsalád	Regression model	Regression model	Regression model
Keletkezés éve	1979	1974	2005
Megalkotó	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics	Good (2005); Edgington & Onghena (2007); resampling tradition
Típus	Resampling-based inference	Robust scale estimator	Nonparametric resampling test
Alapmű	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alternatív nevek	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Kapcsolódó	5	5	5
Összefoglaló	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.	Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.	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.