Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão de Poisson Robusta× | Regressão de Poisson e Binomial Negativa× | |
|---|---|---|
| Área≠ | Estatística | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2004 | 1998 |
| Autor original≠ | Guangyong Zou | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tipo≠ | GLM with robust variance | Generalized linear model for count data |
| Fonte seminal≠ | Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Outros nomes | modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Relacionados≠ | 5 | 4 |
| Resumo≠ | Robust Poisson regression fits a Poisson log-linear model to a binary outcome but replaces the model-based variance with the empirical sandwich estimator. This yields valid standard errors and risk ratios even though Poisson variance assumptions are technically violated for binary data. The approach, popularized by Zou (2004), is widely used in epidemiology as a numerically stable alternative to log-binomial regression. | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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