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| Régression logistique ordonnée (Logit/Probit ordonné)× | Régression par Moindres Carrés Ordinaires (MCO)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1980 | 2019 |
| Auteur d'origine≠ | McCullagh (proportional odds / cumulative model) | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Cumulative ordinal regression | Linear regression |
| Source fondatrice≠ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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