Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle probit bivarié× | Régression logistique ordonnée (Logit/Probit ordonné)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1970 | 1980 |
| Auteur d'origine≠ | J. R. Ashford & R. R. Sowden | McCullagh (proportional odds / cumulative model) |
| Type≠ | Maximum-likelihood binary outcome model | Cumulative ordinal regression |
| Source fondatrice≠ | Ashford, J. R., & Sowden, R. R. (1970). Multi-variate probit analysis. Biometrics, 26(3), 535–546. DOI ↗ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗ |
| Alias≠ | Bivariate Binary Probit, Joint Probit Model, Two-Equation Probit, İki Değişkenli Probit | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit |
| Apparentées≠ | 3 | 4 |
| Résumé≠ | The Bivariate Probit Model, introduced by Ashford and Sowden (1970), jointly estimates two binary outcome equations whose error terms are allowed to be correlated. By modeling both outcomes simultaneously under a bivariate normal distribution, it corrects for the dependence between decisions that separate probit regressions would ignore, producing consistent and efficient parameter estimates for researchers studying interrelated binary choices. | Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes. |
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