Сравнение методов

	Диагностика влияния (расстояние Кука, DFFITS, плечо)	Оценка на основе медианного абсолютного отклонения (MAD)	Квантильная регрессия
Область	Статистика	Статистика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1977	1974	1978
Автор метода	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Hampel (influence-curve treatment); classical robust statistics	Koenker & Bassett
Тип	Regression diagnostic	Robust scale estimator	Conditional quantile regression
Основополагающий источник	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.
Другие названия	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
Связанные	5	5	5
Сводка	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.