Bandingkan metode

	Resampling Jackknife	Estimasi Deviasi Absolut Median (MAD)	Regresi Kuadrat Terkecil Biasa (Ordinary Least Squares - OLS)
Bidang	Statistika	Statistika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1956	1974	2019
Pencetus	Quenouille (1956); reviewed by Miller (1974)	Hampel (influence-curve treatment); classical robust statistics	Wooldridge (textbook treatment); classical least squares
Tipe	Resampling / bias and variance estimation	Robust scale estimator	Linear regression
Sumber perintis	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Terkait	5	5	5
Ringkasan	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.	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).