Confronta i metodi

	Diagnostica di Influenza (Distanza di Cook, DFFITS, Leva)	Regressione quantilica	Regressione Ridge
Campo	Statistica	Econometria	Apprendimento automatico
Famiglia	Regression model	Regression model	Machine learning
Anno di origine	1977	1978	1970
Ideatore	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Koenker & Bassett	Hoerl, A.E. & Kennard, R.W.
Tipo	Regression diagnostic	Conditional quantile regression	L2-regularized linear regression
Fonte seminale	Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	Cook's distance, DFFITS, leverage, influential observation detection	conditional quantile regression, regression quantiles, Kantil Regresyon	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Correlati	5	5	4
Sintesi	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.