Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse de données de panel non linéaires× | Régression logistique× | |
|---|---|---|
| Domaine≠ | Économétrie | Statistiques de recherche |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 1986–2010 | 1958 |
| Auteur d'origine≠ | Cheng Hsiao; Jeffrey M. Wooldridge | David Roxbee Cox |
| Type≠ | Panel data model (nonlinear) | Method |
| Source fondatrice≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias≠ | nonlinear panel models, panel nonlinear econometrics, fixed-effects nonlinear models, random-effects nonlinear models | logit model, binomial logistic regression, LR |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Nonlinear panel data analysis applies nonlinear models — such as probit, logit, Poisson, or Tobit — to repeated observations on the same units over time. It accounts for unit-specific unobserved heterogeneity while capturing non-linear relationships between predictors and the outcome, making it essential when the dependent variable is binary, count-based, censored, or otherwise non-continuous. | 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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