Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Šķēršļu modelis skaitīšanas datiem× | Negatīvā binomiālā regresija× | |
|---|---|---|
| Nozare≠ | Statistika | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 2011 |
| Autors≠ | Mullahy | Hilbe (textbook treatment); generalized linear model framework |
| Tips≠ | Two-part count model | Generalized linear model for count data |
| Pirmavots≠ | Mullahy, J. (1986). Specification and Testing of Some Modified Count Data Models. Journal of Econometrics, 33(3), 341–365. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| Citi nosaukumi≠ | hurdle count model, two-part count model, zero-truncated count model, Engel Modeli (Hurdle Model) | NB regression, NB2 regression, negatif binom regresyonu |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. |
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