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| Régression bêta× | Régression logistique× | |
|---|---|---|
| Domaine≠ | Statistique | Statistiques de recherche |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 2004 | 1958 |
| Auteur d'origine≠ | Ferrari & Cribari-Neto | David Roxbee Cox |
| Type≠ | Generalized linear model (beta distribution) | Method |
| Source fondatrice≠ | Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias | beta regression model, proportion regression, Beta Regresyonu | logit model, binomial logistic regression, LR |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Beta regression is a generalized linear model introduced by Ferrari and Cribari-Neto (2004) for outcomes that are rates or proportions confined to the open interval (0,1). It models the mean of a beta-distributed response through a link function, making it the natural choice for fractions, probability scores, and proportion indices. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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