Compara mètodes

	Diagnòstics d'Influència (Distància de Cook, DFFITS, Palanca)	Estimació per la desviació absoluta mediana (MAD)	Regressió per Mínims Quadrats Ordinàris (MQO)
Camp	Estadística	Estadística	Econometria
Família	Regression model	Regression model	Regression model
Any d'origen	1977	1974	2019
Autor original	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Hampel (influence-curve treatment); classical robust statistics	Wooldridge (textbook treatment); classical least squares
Tipus	Regression diagnostic	Robust scale estimator	Linear regression
Font seminal	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Àlies	Cook's distance, DFFITS, leverage, influential observation detection	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
Relacionats	5	5	5
Resum	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.	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).