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| Zero-Inflated Negative Binomial (ZINB) Regression× | Hurdle-Modell für Zähldaten× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1994 | 1986 |
| Urheber≠ | Greene (1994) | Mullahy |
| Typ≠ | Count regression (mixture model) | Two-part count model |
| Wegweisende Quelle≠ | Greene, W. H. (1994). Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. NYU Working Paper. link ↗ | Mullahy, J. (1986). Specification and Testing of Some Modified Count Data Models. Journal of Econometrics, 33(3), 341–365. DOI ↗ |
| Aliasnamen | ZINB, ZINB regression, zero-inflated negative binomial model, Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB) | hurdle count model, two-part count model, zero-truncated count model, Engel Modeli (Hurdle Model) |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | The hurdle model is a two-part count-data model introduced by Mullahy (1986). A first stage models the binary choice of crossing a hurdle (a zero versus a non-zero count), and a second stage models the strictly positive counts with a zero-truncated distribution such as a zero-truncated Poisson or negative binomial. |
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