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| Corrélation Robuste (Spearman, Kendall et Biweight)× | Régression quantile× | |
|---|---|---|
| Domaine≠ | Statistique | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2012 | 1978 |
| Auteur d'origine≠ | Spearman rank, Kendall tau; biweight from Wilcox / Shevlyakov & Oja robust statistics tradition | Koenker & Bassett |
| Type≠ | Robust correlation measures | Conditional quantile regression |
| Source fondatrice≠ | Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing. Academic Press. ISBN: 978-0123869838 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | Spearman correlation, Kendall tau, biweight midcorrelation, rank correlation | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Apparentées | 5 | 5 |
| Résumé≠ | Robust Correlation is a family of association measures that resist outliers, covering Spearman's rank correlation, Kendall's tau, and the biweight midcorrelation. Drawing on the robust-statistics tradition described by Wilcox (2012) and Shevlyakov & Oja (2016), it measures how strongly two variables move together without being distorted by a few extreme points. | 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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