Linganisha mbinu

	Mbinu ya kusampuli upya ya jackknife	Utoaji wa Hitimisho kwa Njia ya Bootstrap	Uthabiti wa Kiwango cha Thamani ya Kati ya Upotofu kamili (MAD)
Nyanja	Takwimu	Takwimu	Takwimu
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	1956	1979	1974
Mwanzilishi	Quenouille (1956); reviewed by Miller (1974)	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics
Aina	Resampling / bias and variance estimation	Resampling-based inference	Robust scale estimator
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	5	5
Muhtasari	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.