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| Bayesian Negative Binomial Regression× | Null-inflationsmodell× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1990s–2000s | 1992 |
| Urheber≠ | Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi | Diane Lambert |
| Typ≠ | Bayesian GLM for overdispersed counts | Count regression with excess zeros |
| Wegweisende Quelle≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. DOI ↗ |
| Aliasnamen | Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model | ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Bayesian Negative Binomial Regression models non-negative integer count outcomes that exhibit overdispersion — where the variance exceeds the mean — by placing a negative binomial likelihood on the data and specifying prior distributions over the regression coefficients and the dispersion parameter. Posterior inference is typically performed via Markov chain Monte Carlo (MCMC) or variational methods, yielding full posterior distributions rather than point estimates. | 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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