statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...

P(N(D)=k)={\frac {(\lambda |D|)^{k}e^{-\lambda |D|}}{k!}}.} Poisson regression and negative binomial regression are useful for analyses where the dependent (response)...

overdispersed alternative to the Poisson distribution, for example for a robust modification of Poisson regression. In epidemiology, it has been used...

j ∼ Poisson ( λ ) Y i , j ∼ Poisson ( μ ) {\displaystyle {\begin{aligned}X_{i,j}&\sim {\text{Poisson}}(\lambda )\\Y_{i,j}&\sim {\text{Poisson}}(\mu...

model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is...

the exponential regression framework, which the following discussion follows closely. While the example focuses on a Poisson regression model, it is possible...

various other statistical models, including linear regression, logistic regression and Poisson regression. They proposed an iteratively reweighted least squares...

linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where...

is often represented using a Poisson distribution or a negative binomial distribution. Hilbe notes that "Poisson regression is traditionally conceived of...

In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...