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| Diagnostics d'influence (Distance de Cook, DFFITS, Effet de levier)× | Régression quantile× | |
|---|---|---|
| Domaine≠ | Statistique | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1977 | 1978 |
| Auteur d'origine≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Koenker & Bassett |
| Type≠ | Regression diagnostic | Conditional quantile regression |
| Source fondatrice≠ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | Cook's distance, DFFITS, leverage, influential observation detection | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. |
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