title: Maximum likelihood estimation and testing of a poisson regression model.
abstract: A Poisson regression model is proposed for the analysis of incidence rates presented in a two-way table classified by two categorical variables. It is shown that the likelihood function is the same as that using Glasser's exponential covariate model. An algorithm is given to solve the maximum likelihood estimates of the regression parameters. The model is evaluated via deviance and the method is illustrated with an example. Some extensions of the model are discussed.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor.
authors.forename: J Y
authors.initials: JY
authors.lastname: Wan
authors.forename: A T
authors.initials: AT
authors.lastname: Galecki
mesh_terms: D000328:Adult; D000368:Aged; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008297:Male; D008432:Mathematical Computing; D008545:Melanoma / Q000453:epidemiology; D008875:Middle Aged; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D012306:Risk; D014481:United States / Q000453:epidemiology