Porovnat metody

	Diagnostika vlivu (Cookova vzdálenost, DFFITS, pákový efekt)	Odhad mediánovou absolutní odchylkou (MAD)	Kvantilová regrese
Obor	Statistika	Statistika	Ekonometrie
Rodina	Regression model	Regression model	Regression model
Rok vzniku	1977	1974	1978
Tvůrce	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Hampel (influence-curve treatment); classical robust statistics	Koenker & Bassett
Typ	Regression diagnostic	Robust scale estimator	Conditional quantile regression
Původní zdroj	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Další názvy	Cook's distance, DFFITS, leverage, influential observation detection	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	conditional quantile regression, regression quantiles, Kantil Regresyon
Příbuzné	5	5	5
Shrnutí	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.	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.