Krahasoni metodat

	Rikthimi me anashkrim (Jackknife Resampling)	Inferenca Bootstrap	Vlerësimi i Devijimit Mesor Absolut (MAD)
Fusha	Statistikë	Statistikë	Statistikë
Familja	Regression model	Regression model	Regression model
Viti i origjinës	1956	1979	1974
Krijuesi	Quenouille (1956); reviewed by Miller (1974)	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics
Lloji	Resampling / bias and variance estimation	Resampling-based inference	Robust scale estimator
Burimi themelues	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	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.
Emërtime të tjera	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini
Të lidhura	5	5	5
Përmbledhja	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.	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.