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| Modèle de régression probit× | Régression logistique× | |
|---|---|---|
| Domaine≠ | Économétrie | Statistiques de recherche |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 2018 | 1958 |
| Auteur d'origine≠ | Greene (textbook treatment); classical discrete-choice modelling | David Roxbee Cox |
| Type≠ | Binary discrete-choice model | Method |
| Source fondatrice≠ | Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias | probit regression, normit model, Probit Modeli | logit model, binomial logistic regression, LR |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018). | 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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