Poisson 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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.