เปรียบเทียบวิธี

	การวินิจฉัยอิทธิพล (ระยะห่างของคุ้ก, DFFITS, Leverage)	การประมาณค่าส่วนเบี่ยงเบนสัมบูรณ์มัธยฐาน (MAD)	Ridge Regression
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์	การเรียนรู้ของเครื่อง
ตระกูล	Regression model	Regression model	Machine learning
ปีกำเนิด	1977	1974	1970
ผู้ริเริ่ม	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Hampel (influence-curve treatment); classical robust statistics	Hoerl, A.E. & Kennard, R.W.
ประเภท	Regression diagnostic	Robust scale estimator	L2-regularized linear regression
แหล่งต้นตำรับ	Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
ชื่อเรียกอื่น	Cook's distance, DFFITS, leverage, influential observation detection	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
ที่เกี่ยวข้อง	5	5	4
สรุป	Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients.	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.