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| Robuste negative binomiale Regression× | Null-inflationsmodell× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2000s–2011 | 1992 |
| Urheber≠ | Hilbe, J. M.; Zeileis, A. et al. | Diane Lambert |
| Typ≠ | Count regression with robust inference | Count regression with excess zeros |
| Wegweisende Quelle≠ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158 | Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. DOI ↗ |
| Aliasnamen | robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression | ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Robust Negative Binomial Regression models overdispersed count outcomes using the negative binomial distribution while protecting coefficient inference against misspecification of the variance function. It pairs maximum-likelihood estimation of the mean and dispersion parameters with sandwich (Huber-White) standard errors, yielding valid tests even when the assumed variance structure is only approximately correct. | A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generates structural zeros; the other is a count process that generates both zeros and positive counts. |
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