เปรียบเทียบวิธี

	การสุ่มตัวอย่างแบบแจ็คไนฟ์ (Jackknife Resampling)	การประมาณค่าส่วนเบี่ยงเบนสัมบูรณ์มัธยฐาน (MAD)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	1956	1974	2019
ผู้ริเริ่ม	Quenouille (1956); reviewed by Miller (1974)	Hampel (influence-curve treatment); classical robust statistics	Wooldridge (textbook treatment); classical least squares
ประเภท	Resampling / bias and variance estimation	Robust scale estimator	Linear regression
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	5	5	5
สรุป	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).