Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Logística Ordinal Bayesiana× | Regressão Logística Multinomial× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1999 | 1966–1974 |
| Autor original≠ | Johnson & Albert (1999); Bayesian proportional odds framework | Cox (1966); Theil (1969); formalized by McFadden (1974) |
| Tipo≠ | Bayesian generalized linear model | Generalized linear model |
| Fonte seminal≠ | Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484 | Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933 |
| Outros nomes | Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model | polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | Bayesian ordinal logistic regression extends the classical proportional odds model by placing prior distributions on the regression coefficients and threshold parameters and updating them with observed data via Bayes' theorem. The result is a full posterior distribution over all parameters, enabling uncertainty quantification without relying on large-sample approximations. | Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels. |
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