Regression model
Negative Binomial Regression
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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Sources
- Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI: 10.1017/CBO9780511973420 ↗
Related methods
Referenced by
Bayesian Negative Binomial RegressionBayesian Poisson RegressionBayesian single-cell RNA-seq analysisBonus-Malus SystemGamma RegressionGeneralized Linear ModelHurdle ModelMultinomial LogitOrdered LogitRecurrent Event ModelRobust Negative Binomial RegressionRobust Poisson RegressionTobit ModelZero-inflated modelZero-Inflated Negative Binomial RegressionZero-Inflated Poisson Regression