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| Regressione Binomiale Negativa con Inflazione di Zeri (ZINB)× | Regressione di Poisson e Binomiale Negativa× | |
|---|---|---|
| Campo≠ | Statistica | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1994 | 1998 |
| Ideatore≠ | Greene (1994) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tipo≠ | Count regression (mixture model) | Generalized linear model for count data |
| Fonte seminale≠ | Greene, W. H. (1994). Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. NYU Working Paper. link ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Alias | ZINB, ZINB regression, zero-inflated negative binomial model, Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB) | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | Zero-Inflated Negative Binomial regression is a count model, introduced by Greene (1994), that handles count data showing both an excess of zeros and overdispersion. It combines a binary inflation process that generates structural zeros with a negative binomial count process, making it one of the most widely used distributions for real-world count data. | 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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