Regression model
Poisson and Negative Binomial Regression
Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.
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Sources
- Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI: 10.1017/CBO9780511814365 ↗
- 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 Zero-inflated modelCapture-RecaptureCroston's MethodGamma RegressionGeneralized Linear ModelHurdle ModelMultinomial LogitNegative Binomial RegressionNonlinear Panel Data AnalysisOrdinal RegressionQuantile RegressionRecurrent Event ModelRobust Negative Binomial RegressionRobust Poisson RegressionSpatial Interaction ModelZero-inflated modelZero-Inflated Negative Binomial RegressionZero-Inflated Poisson Regression