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| Procédure de Benjamini-Hochberg (Contrôle du FDR)× | Régression linéaire multiple× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille≠ | Hypothesis test | Regression model |
| Année d'origine≠ | 1995 | 1886 |
| Auteur d'origine≠ | Yoav Benjamini & Yosef Hochberg | Francis Galton; formalized by Karl Pearson |
| Type≠ | False discovery rate (FDR) procedure | Parametric linear model |
| Source fondatrice≠ | Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289–300. DOI ↗ | Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263. DOI ↗ |
| Alias≠ | BH procedure, FDR control, false discovery rate procedure, Benjamini-Hochberg düzeltmesi | MLR, OLS regression, multiple regression, linear regression with multiple predictors |
| Apparentées≠ | 3 | 8 |
| Résumé≠ | The Benjamini-Hochberg (BH) procedure, introduced by Yoav Benjamini and Yosef Hochberg in 1995, controls the false discovery rate (FDR) — the expected proportion of false positives among all rejected hypotheses — rather than the probability of any false positive. By tolerating a controlled fraction of false discoveries, it delivers far greater power than family-wise error rate methods such as Bonferroni or Holm, which is why it has become the standard tool for large-scale simultaneous testing in genomics, neuroimaging, and other high-throughput fields. | Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression. |
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